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25 package jdk.incubator.vector;
26
27 import java.nio.ByteBuffer;
28 import java.nio.ByteOrder;
29 import java.util.Arrays;
30
31 /**
32 * A
33 *
34 * <!-- The following paragraphs are shared verbatim
35 * -- between Vector.java and package-info.java -->
36 * sequence of a fixed number of <em>lanes</em>,
37 * all of some fixed
38 * {@linkplain Vector#elementType() <em>element type</em>}
39 * such as {@code byte}, {@code long}, or {@code float}.
40 * Each lane contains an independent value of the element type.
41 * Operations on vectors are typically
42 * <a href="Vector.html#lane-wise"><em>lane-wise</em></a>,
43 * distributing some scalar operator (such as
44 * {@linkplain Vector#add(Vector) addition})
45 * across the lanes of the participating vectors,
46 * usually generating a vector result whose lanes contain the various
47 * scalar results. When run on a supporting platform, lane-wise
48 * operations can be executed in parallel by the hardware. This style
49 * of parallelism is called <em>Single Instruction Multiple Data</em>
50 * (SIMD) parallelism.
51 *
52 * <p> In the SIMD style of programming, most of the operations within
53 * a vector lane are unconditional, but the effect of conditional
54 * execution may be achieved using
55 * <a href="Vector.html#masking"><em>masked operations</em></a>
56 * such as {@link Vector#blend(Vector,VectorMask) blend()},
57 * under the control of an associated {@link VectorMask}.
58 * Data motion other than strictly lane-wise flow is achieved using
59 * <a href="Vector.html#cross-lane"><em>cross-lane</em></a>
60 * operations, often under the control of an associated
61 * {@link VectorShuffle}.
62 * Lane data and/or whole vectors can be reformatted using various
63 * kinds of lane-wise
64 * {@linkplain Vector#convert(VectorOperators.Conversion,int) conversions},
65 * and byte-wise reformatting
66 * {@linkplain Vector#reinterpretShape(VectorSpecies,int) reinterpretations},
67 * often under the control of a reflective {@link VectorSpecies}
68 * object which selects an alternative vector format different
69 * from that of the input vector.
70 *
71 * <p> {@code Vector<E>} declares a set of vector operations (methods)
72 * that are common to all element types. These common operations
73 * include generic access to lane values, data selection and movement,
74 * reformatting, and certain arithmetic and logical operations (such as addition
75 * or comparison) that are common to all primitive types.
76 *
77 * <p> <a href="Vector.html#subtypes">Public subtypes of {@code Vector}</a>
78 * correspond to specific
79 * element types. These declare further operations that are specific
80 * to that element type, including unboxed access to lane values,
81 * bitwise operations on values of integral element types, or
82 * transcendental operations on values of floating point element
83 * types.
84 *
85 * <p> Some lane-wise operations, such as the {@code add} operator, are defined as
86 * a full-service named operation, where a corresponding method on {@code Vector}
87 * comes in masked and unmasked overloadings, and (in subclasses) also comes in
88 * covariant overrides (returning the subclass) and additional scalar-broadcast
89 * overloadings (both masked and unmasked).
90 *
91 * Other lane-wise operations, such as the {@code min} operator, are defined as a
92 * partially serviced (not a full-service) named operation, where a corresponding
93 * method on {@code Vector} and/or a subclass provide some but all possible
94 * overloadings and overrides (commonly the unmasked varient with scalar-broadcast
95 * overloadings).
96 *
97 * Finally, all lane-wise operations (those named as previously described,
98 * or otherwise unnamed method-wise) have a corresponding
99 * {@link VectorOperators.Operator operator token}
100 * declared as a static constant on {@link VectorOperators}.
101 * Each operator token defines a symbolic Java expression for the operation,
102 * such as {@code a + b} for the
103 * {@link VectorOperators#ADD ADD} operator token.
104 * General lane-wise operation-token accepting methods, such as for a
105 * {@linkplain Vector#lanewise(VectorOperators.Unary) unary lane-wise}
106 * operation, are provided on {@code Vector} and come in the same variants as
107 * a full-service named operation.
108 *
109 * <p>This package contains a public subtype of {@link Vector}
110 * corresponding to each supported element type:
111 * {@link ByteVector}, {@link ShortVector},
112 * {@link IntVector}, {@link LongVector},
113 * {@link FloatVector}, and {@link DoubleVector}.
114 *
115 * <!-- The preceding paragraphs are shared verbatim
116 * -- between Vector.java and package-info.java -->
117 *
118 * <p><a id="ETYPE"></a> The {@linkplain #elementType element type} of a vector,
119 * referred to as {@code ETYPE}, is one of the primitive types
120 * {@code byte}, {@code short}, {@code int}, {@code long}, {@code
121 * float}, or {@code double}.
122 *
123 * <p> The type {@code E} in {@code Vector<E>} is the <em>boxed</em> version
124 * of {@code ETYPE}. For example, in the type {@code Vector<Integer>}, the {@code E}
125 * parameter is {@code Integer} and the {@code ETYPE} is {@code int}. In such a
126 * vector, each lane carries a primitive {@code int} value. This pattern continues
127 * for the other primitive types as well. (See also sections {@jls 5.1.7} and
128 * {@jls 5.1.8} of the <cite>The Java Language Specification</cite>.)
129 *
130 * <p><a id="VLENGTH"></a> The {@linkplain #length() length} of a vector
131 * is the lane count, the number of lanes it contains.
132 *
133 * This number is also called {@code VLENGTH} when the context makes
134 * clear which vector it belongs to. Each vector has its own fixed
135 * {@code VLENGTH} but different instances of vectors may have
136 * different lengths. {@code VLENGTH} is an important number, because
137 * it estimates the SIMD performance gain of a single vector operation
138 * as compared to scalar execution of the {@code VLENGTH} scalar
139 * operators which underly the vector operation.
140 *
141 * <h2><a id="species"></a>Shapes and species</h2>
142 *
143 * The information capacity of a vector is determined by its
144 * {@linkplain #shape() <em>vector shape</em>}, also called its
145 * {@code VSHAPE}. Each possible {@code VSHAPE} is represented by
146 * a member of the {@link VectorShape} enumeration, and represents
147 * an implementation format shared in common by all vectors of
148 * that shape. Thus, the {@linkplain #bitSize() size in bits} of
149 * of a vector is determined by appealing to its vector shape.
150 *
151 * <p> Some Java platforms give special support to only one shape,
152 * while others support several. A typical platform is not likely
153 * to support all the shapes described by this API. For this reason,
154 * most vector operations work on a single input shape and
155 * produce the same shape on output. Operations which change
156 * shape are clearly documented as such <em>shape-changing</em>,
157 * while the majority of operations are <em>shape-invariant</em>,
158 * to avoid disadvantaging platforms which support only one shape.
159 * There are queries to discover, for the current Java platform,
160 * the {@linkplain VectorShape#preferredShape() preferred shape}
161 * for general SIMD computation, or the
162 * {@linkplain VectorShape#largestShapeFor(Class) largest
163 * available shape} for any given lane type. To be portable,
164 * code using this API should start by querying a supported
165 * shape, and then process all data with shape-invariant
166 * operations, within the selected shape.
167 *
168 * <p> Each unique combination of element type and vector shape
169 * determines a unique
170 * {@linkplain #species() <em>vector species</em>}.
171 * A vector species is represented by a fixed instance of
172 * {@link VectorSpecies VectorSpecies<E>}
173 * shared in common by all vectors of the same shape and
174 * {@code ETYPE}.
175 *
176 * <p> Unless otherwise documented, lane-wise vector operations
177 * require that all vector inputs have exactly the same {@code VSHAPE}
178 * and {@code VLENGTH}, which is to say that they must have exactly
179 * the same species. This allows corresponding lanes to be paired
180 * unambiguously. The {@link #check(VectorSpecies) check()} method
181 * provides an easy way to perform this check explicitly.
182 *
183 * <p> Vector shape, {@code VLENGTH}, and {@code ETYPE} are all
184 * mutually constrained, so that {@code VLENGTH} times the
185 * {@linkplain #elementSize() bit-size of each lane}
186 * must always match the bit-size of the vector's shape.
187 *
188 * Thus, {@linkplain #reinterpretShape(VectorSpecies,int) reinterpreting} a
189 * vector may double its length if and only if it either halves the lane size,
190 * or else changes the shape. Likewise, reinterpreting a vector may double the
191 * lane size if and only if it either halves the length, or else changes the
192 * shape of the vector.
193 *
194 * <h2><a id="subtypes"></a>Vector subtypes</h2>
195 *
196 * Vector declares a set of vector operations (methods) that are common to all
197 * element types (such as addition). Sub-classes of Vector with a concrete
198 * element type declare further operations that are specific to that
199 * element type (such as access to element values in lanes, logical operations
200 * on values of integral elements types, or transcendental operations on values
201 * of floating point element types).
202 * There are six abstract sub-classes of Vector corresponding to the supported set
203 * of element types, {@link ByteVector}, {@link ShortVector},
204 * {@link IntVector}, {@link LongVector}, {@link FloatVector}, and
205 * {@link DoubleVector}. Along with type-specific operations these classes
206 * support creation of vector values (instances of Vector).
207 * They expose static constants corresponding to the supported species,
208 * and static methods on these types generally take a species as a parameter.
209 * For example,
210 * {@link FloatVector#fromArray(VectorSpecies, float[], int) FloatVector.fromArray}
211 * creates and returns a float vector of the specified species, with elements
212 * loaded from the specified float array.
213 * It is recommended that Species instances be held in {@code static final}
214 * fields for optimal creation and usage of Vector values by the runtime compiler.
215 *
216 * <p> As an example of static constants defined by the typed vector classes,
217 * constant {@link FloatVector#SPECIES_256 FloatVector.SPECIES_256}
218 * is the unique species whose lanes are {@code float}s and whose
219 * vector size is 256 bits. Again, the constant
220 * {@link FloatVector#SPECIES_PREFERRED} is the species which
221 * best supports processing of {@code float} vector lanes on
222 * the currently running Java platform.
223 *
224 * <p> As another example, a broadcast scalar value of
225 * {@code (double)0.5} can be obtained by calling
226 * {@link DoubleVector#broadcast(VectorSpecies,double)
227 * DoubleVector.broadcast(dsp, 0.5)}, but the argument {@code dsp} is
228 * required to select the species (and hence the shape and length) of
229 * the resulting vector.
230 *
231 * <h2><a id="lane-wise"></a>Lane-wise operations</h2>
232 *
233 * We use the term <em>lanes</em> when defining operations on
234 * vectors. The number of lanes in a vector is the number of scalar
235 * elements it holds. For example, a vector of type {@code float} and
236 * shape {@code S_256_BIT} has eight lanes, since {@code 32*8=256}.
237 *
238 * <p> Most operations on vectors are lane-wise, which means the operation
239 * is composed of an underlying scalar operator, which is repeated for
240 * each distinct lane of the input vector. If there are additional
241 * vector arguments of the same type, their lanes are aligned with the
242 * lanes of the first input vector. (They must all have a common
243 * {@code VLENGTH}.) For most lane-wise operations, the output resulting
244 * from a lane-wise operation will have a {@code VLENGTH} which is equal to
245 * the {@code VLENGTH} of the input(s) to the operation. Thus, such lane-wise
246 * operations are <em>length-invariant</em>, in their basic definitions.
247 *
248 * <p> The principle of length-invariance is combined with another
249 * basic principle, that most length-invariant lane-wise operations are also
250 * <em>shape-invariant</em>, meaning that the inputs and the output of
251 * a lane-wise operation will have a common {@code VSHAPE}. When the
252 * principles conflict, because a logical result (with an invariant
253 * {@code VLENGTH}), does not fit into the invariant {@code VSHAPE},
254 * the resulting expansions and contractions are handled explicitly
255 * with
256 * <a href="Vector.html#expansion">special conventions</a>.
257 *
258 * <p> Vector operations can be grouped into various categories and
259 * their behavior can be generally specified in terms of underlying
260 * scalar operators. In the examples below, {@code ETYPE} is the
261 * element type of the operation (such as {@code int.class}) and
262 * {@code EVector} is the corresponding concrete vector type (such as
263 * {@code IntVector.class}).
264 *
265 * <ul>
266 * <li>
267 * A <em>lane-wise unary</em> operation, such as
268 * {@code w = v0.}{@link Vector#neg() neg}{@code ()},
269 * takes one input vector,
270 * distributing a unary scalar operator across the lanes,
271 * and produces a result vector of the same type and shape.
272 *
273 * For each lane of the input vector {@code a},
274 * the underlying scalar operator is applied to the lane value.
275 * The result is placed into the vector result in the same lane.
276 * The following pseudocode illustrates the behavior of this operation
277 * category:
278 *
279 * <pre>{@code
280 * ETYPE scalar_unary_op(ETYPE s);
281 * EVector a = ...;
282 * VectorSpecies<E> species = a.species();
283 * ETYPE[] ar = new ETYPE[a.length()];
284 * for (int i = 0; i < ar.length; i++) {
285 * ar[i] = scalar_unary_op(a.lane(i));
286 * }
287 * EVector r = EVector.fromArray(species, ar, 0);
288 * }</pre>
289 *
290 * <li>
291 * A <em>lane-wise binary</em> operation, such as
292 * {@code w = v0.}{@link Vector#add(Vector) add}{@code (v1)},
293 * takes two input vectors,
294 * distributing a binary scalar operator across the lanes,
295 * and produces a result vector of the same type and shape.
296 *
297 * For each lane of the two input vectors {@code a} and {@code b},
298 * the underlying scalar operator is applied to the lane values.
299 * The result is placed into the vector result in the same lane.
300 * The following pseudocode illustrates the behavior of this operation
301 * category:
302 *
303 * <pre>{@code
304 * ETYPE scalar_binary_op(ETYPE s, ETYPE t);
305 * EVector a = ...;
306 * VectorSpecies<E> species = a.species();
307 * EVector b = ...;
308 * b.check(species); // must have same species
309 * ETYPE[] ar = new ETYPE[a.length()];
310 * for (int i = 0; i < ar.length; i++) {
311 * ar[i] = scalar_binary_op(a.lane(i), b.lane(i));
312 * }
313 * EVector r = EVector.fromArray(species, ar, 0);
314 * }</pre>
315 * </li>
316 *
317 * <li>
318 * Generalizing from unary and binary operations,
319 * a <em>lane-wise n-ary</em> operation takes {@code N} input vectors {@code v[j]},
320 * distributing an n-ary scalar operator across the lanes,
321 * and produces a result vector of the same type and shape.
322 * Except for a few ternary operations, such as
323 * {@code w = v0.}{@link FloatVector#fma(Vector,Vector) fma}{@code (v1,v2)},
324 * this API has no support for
325 * lane-wise n-ary operations.
326 *
327 * For each lane of all of the input vectors {@code v[j]},
328 * the underlying scalar operator is applied to the lane values.
329 * The result is placed into the vector result in the same lane.
330 * The following pseudocode illustrates the behavior of this operation
331 * category:
332 *
333 * <pre>{@code
334 * ETYPE scalar_nary_op(ETYPE... args);
335 * EVector[] v = ...;
336 * int N = v.length;
337 * VectorSpecies<E> species = v[0].species();
338 * for (EVector arg : v) {
339 * arg.check(species); // all must have same species
340 * }
341 * ETYPE[] ar = new ETYPE[a.length()];
342 * for (int i = 0; i < ar.length; i++) {
343 * ETYPE[] args = new ETYPE[N];
344 * for (int j = 0; j < N; j++) {
345 * args[j] = v[j].lane(i);
346 * }
347 * ar[i] = scalar_nary_op(args);
348 * }
349 * EVector r = EVector.fromArray(species, ar, 0);
350 * }</pre>
351 * </li>
352 *
353 * <li>
354 * A <em>lane-wise conversion</em> operation, such as
355 * {@code w0 = v0.}{@link
356 * Vector#convert(VectorOperators.Conversion,int)
357 * convert}{@code (VectorOperators.I2D, 0)},
358 * takes one input vector,
359 * distributing a unary scalar conversion operator across the lanes,
360 * and produces a logical result of the converted values. The logical
361 * result (or at least a part of it) is presented in a vector of the
362 * same shape as the input vector.
363 *
364 * <p> Unlike other lane-wise operations, conversions can change lane
365 * type, from the input (domain) type to the output (range) type. The
366 * lane size may change along with the type. In order to manage the
367 * size changes, lane-wise conversion methods can product <em>partial
368 * results</em>, under the control of a {@code part} parameter, which
369 * is <a href="Vector.html#expansion">explained elsewhere</a>.
370 * (Following the example above, the second group of converted lane
371 * values could be obtained as
372 * {@code w1 = v0.convert(VectorOperators.I2D, 1)}.)
373 *
374 * <p> The following pseudocode illustrates the behavior of this
375 * operation category in the specific example of a conversion from
376 * {@code int} to {@code double}, retaining either lower or upper
377 * lanes (depending on {@code part}) to maintain shape-invariance:
378 *
379 * <pre>{@code
380 * IntVector a = ...;
381 * int VLENGTH = a.length();
382 * int part = ...; // 0 or 1
383 * VectorShape VSHAPE = a.shape();
384 * double[] arlogical = new double[VLENGTH];
385 * for (int i = 0; i < limit; i++) {
386 * int e = a.lane(i);
387 * arlogical[i] = (double) e;
388 * }
389 * VectorSpecies<Double> rs = VSHAPE.withLanes(double.class);
390 * int M = Double.BITS / Integer.BITS; // expansion factor
391 * int offset = part * (VLENGTH / M);
392 * DoubleVector r = DoubleVector.fromArray(rs, arlogical, offset);
393 * assert r.length() == VLENGTH / M;
394 * }</pre>
395 * </li>
396 *
397 * <li>
398 * A <em>cross-lane reduction</em> operation, such as
399 * {@code e = v0.}{@link
400 * IntVector#reduceLanes(VectorOperators.Associative)
401 * reduceLanes}{@code (VectorOperators.ADD)},
402 * operates on all
403 * the lane elements of an input vector.
404 * An accumulation function is applied to all the
405 * lane elements to produce a scalar result.
406 * If the reduction operation is associative then the result may be accumulated
407 * by operating on the lane elements in any order using a specified associative
408 * scalar binary operation and identity value. Otherwise, the reduction
409 * operation specifies the order of accumulation.
410 * The following pseudocode illustrates the behavior of this operation category
411 * if it is associative:
412 * <pre>{@code
413 * ETYPE assoc_scalar_binary_op(ETYPE s, ETYPE t);
414 * EVector a = ...;
415 * ETYPE r = <identity value>;
416 * for (int i = 0; i < a.length(); i++) {
417 * r = assoc_scalar_binary_op(r, a.lane(i));
418 * }
419 * }</pre>
420 * </li>
421 *
422 * <li>
423 * A <em>cross-lane movement</em> operation, such as
424 * {@code w = v0.}{@link
425 * Vector#rearrange(VectorShuffle) rearrange}{@code (shuffle)}
426 * operates on all
427 * the lane elements of an input vector and moves them
428 * in a data-dependent manner into <em>different lanes</em>
429 * in an output vector.
430 * The movement is steered by an auxiliary datum, such as
431 * a {@link VectorShuffle} or a scalar index defining the
432 * origin of the movement.
433 * The following pseudocode illustrates the behavior of this
434 * operation category, in the case of a shuffle:
435 * <pre>{@code
436 * EVector a = ...;
437 * Shuffle<E> s = ...;
438 * ETYPE[] ar = new ETYPE[a.length()];
439 * for (int i = 0; i < ar.length; i++) {
440 * int source = s.laneSource(i);
441 * ar[i] = a.lane(source);
442 * }
443 * EVector r = EVector.fromArray(a.species(), ar, 0);
444 * }</pre>
445 * </li>
446 *
447 * <li>
448 * A <em>masked operation</em> is one which is a variation on one of the
449 * previous operations (either lane-wise or cross-lane), where
450 * the operation takes an extra trailing {@link VectorMask} argument.
451 * In lanes the mask is set, the operation behaves as if the mask
452 * argument were absent, but in lanes where the mask is unset, the
453 * underlying scalar operation is suppressed.
454 * Masked operations are explained in
455 * <a href="Vector.html#masking">greater detail elsewhere</a>.
456 * </li>
457 *
458 * <li>
459 * A very special case of a masked lane-wise binary operation is a
460 * {@linkplain #blend(Vector,VectorMask) blend}, which operates
461 * lane-wise on two input vectors {@code a} and {@code b}, selecting lane
462 * values from one input or the other depending on a mask {@code m}.
463 * In lanes where {@code m} is set, the corresponding value from
464 * {@code b} is selected into the result; otherwise the value from
465 * {@code a} is selected. Thus, a blend acts as a vectorized version
466 * of Java's ternary selection expression {@code m?b:a}:
467 * <pre>{@code
468 * ETYPE[] ar = new ETYPE[a.length()];
469 * for (int i = 0; i < ar.length; i++) {
470 * boolean isSet = m.laneIsSet(i);
471 * ar[i] = isSet ? b.lane(i) : a.lane(i);
472 * }
473 * EVector r = EVector.fromArray(species, ar, 0);
474 * }</pre>
475 * </li>
476 *
477 * <li>
478 * A <em>lane-wise binary test</em> operation, such as
479 * {@code m = v0.}{@link Vector#lt(Vector) lt}{@code (v1)},
480 * takes two input vectors,
481 * distributing a binary scalar comparison across the lanes,
482 * and produces, not a vector of booleans, but rather a
483 * {@linkplain VectorMask vector mask}.
484 *
485 * For each lane of the two input vectors {@code a} and {@code b},
486 * the underlying scalar comparison operator is applied to the lane values.
487 * The resulting boolean is placed into the vector mask result in the same lane.
488 * The following pseudocode illustrates the behavior of this operation
489 * category:
490 * <pre>{@code
491 * boolean scalar_binary_test_op(ETYPE s, ETYPE t);
492 * EVector a = ...;
493 * VectorSpecies<E> species = a.species();
494 * EVector b = ...;
495 * b.check(species); // must have same species
496 * boolean[] mr = new boolean[a.length()];
497 * for (int i = 0; i < mr.length; i++) {
498 * mr[i] = scalar_binary_test_op(a.lane(i), b.lane(i));
499 * }
500 * VectorMask<E> m = VectorMask.fromArray(species, mr, 0);
501 * }</pre>
502 * </li>
503 *
504 * <li>
505 * Similarly to a binary comparison, a <em>lane-wise unary test</em>
506 * operation, such as
507 * {@code m = v0.}{@link Vector#test(VectorOperators.Test)
508 * test}{@code (IS_FINITE)},
509 * takes one input vector, distributing a scalar predicate
510 * (a test function) across the lanes, and produces a
511 * {@linkplain VectorMask vector mask}.
512 * </li>
513 *
514 * </ul>
515 *
516 * <p>
517 * If a vector operation does not belong to one of the above categories then
518 * the method documentation explicitly specifies how it processes the lanes of
519 * input vectors, and where appropriate illustrates the behavior using
520 * pseudocode.
521 *
522 * <p>
523 * Most lane-wise binary and comparison operations offer convenience
524 * overloadings which accept a scalar as the second input, in place of a
525 * vector. In this case the scalar value is promoted to a vector by
526 * {@linkplain Vector#broadcast(long) broadcasting it}
527 * into the same lane structure as the first input.
528 *
529 * For example, to multiply all lanes of a {@code double} vector by
530 * a scalar value {@code 1.1}, the expression {@code v.mul(1.1)} is
531 * easier to work with than an equivalent expression with an explicit
532 * broadcast operation, such as {@code v.mul(v.broadcast(1.1))}
533 * or {@code v.mul(DoubleVector.broadcast(v.species(), 1.1))}.
534 *
535 * Unless otherwise specified the scalar variant always behaves as if
536 * each scalar value is first transformed to a vector of the same
537 * species as the first vector input, using the appropriate
538 * {@code broadcast} operation.
539 *
540 * <h2><a id="masking"></a>Masked operations</h2>
541 *
542 * <p> Many vector operations accept an optional
543 * {@link VectorMask mask} argument, selecting which lanes participate
544 * in the underlying scalar operator. If present, the mask argument
545 * appears at the end of the method argument list.
546 *
547 * <p> Each lane of the mask argument is a boolean which is either in
548 * the <em>set</em> or <em>unset</em> state. For lanes where the mask
549 * argument is unset, the underlying scalar operator is suppressed.
550 * In this way, masks allow vector operations to emulate scalar
551 * control flow operations, without losing SIMD parallelism, except
552 * where the mask lane is unset.
553 *
554 * <p> An operation suppressed by a mask will never cause an exception
555 * or side effect of any sort, even if the underlying scalar operator
556 * can potentially do so. For example, an unset lane that seems to
557 * access an out of bounds array element or divide an integral value
558 * by zero will simply be ignored. Values in suppressed lanes never
559 * participate or appear in the result of the overall operation.
560 *
561 * <p> Result lanes corresponding to a suppressed operation will be
562 * filled with a default value which depends on the specific
563 * operation, as follows:
564 *
565 * <ul>
566 *
567 * <li>If the masked operation is a unary, binary, or n-ary arithmetic or
568 * logical operation, suppressed lanes are filled from the first
569 * vector operand (i.e., the vector receiving the method call), as if
570 * by a {@linkplain #blend(Vector,VectorMask) blend}.</li>
571 *
572 * <li>If the masked operation is a memory load or a {@code slice()} from
573 * another vector, suppressed lanes are not loaded, and are filled
574 * with the default value for the {@code ETYPE}, which in every case
575 * consists of all zero bits. An unset lane can never cause an
576 * exception, even if the hypothetical corresponding memory location
577 * does not exist (because it is out of an array's index range).</li>
578 *
579 * <li>If the operation is a cross-lane operation with an operand
580 * which supplies lane indexes (of type {@code VectorShuffle} or
581 * {@code Vector}, suppressed lanes are not computed, and are filled
582 * with the zero default value. Normally, invalid lane indexes elicit
583 * an {@code IndexOutOfBoundsException}, but if a lane is unset, the
584 * zero value is quietly substituted, regardless of the index. This
585 * rule is similar to the previous rule, for masked memory loads.</li>
586 *
587 * <li>If the masked operation is a memory store or an {@code unslice()} into
588 * another vector, suppressed lanes are not stored, and the
589 * corresponding memory or vector locations (if any) are unchanged.
590 *
591 * <p> (Note: Memory effects such as race conditions never occur for
592 * suppressed lanes. That is, implementations will not secretly
593 * re-write the existing value for unset lanes. In the Java Memory
594 * Model, reassigning a memory variable to its current value is not a
595 * no-op; it may quietly undo a racing store from another
596 * thread.)</p>
597 * </li>
598 *
599 * <li>If the masked operation is a reduction, suppressed lanes are ignored
600 * in the reduction. If all lanes are suppressed, a suitable neutral
601 * value is returned, depending on the specific reduction operation,
602 * and documented by the masked variant of that method. (This means
603 * that users can obtain the neutral value programmatically by
604 * executing the reduction on a dummy vector with an all-unset mask.)
605 *
606 * <li>If the masked operation is a comparison operation, suppressed output
607 * lanes in the resulting mask are themselves unset, as if the
608 * suppressed comparison operation returned {@code false} regardless
609 * of the suppressed input values. In effect, it is as if the
610 * comparison operation were performed unmasked, and then the
611 * result intersected with the controlling mask.</li>
612 *
613 * <li>In other cases, such as masked
614 * <a href="Vector.html#cross-lane"><em>cross-lane movements</em></a>,
615 * the specific effects of masking are documented by the masked
616 * variant of the method.
617 *
618 * </ul>
619 *
620 * <p> As an example, a masked binary operation on two input vectors
621 * {@code a} and {@code b} suppresses the binary operation for lanes
622 * where the mask is unset, and retains the original lane value from
623 * {@code a}. The following pseudocode illustrates this behavior:
624 * <pre>{@code
625 * ETYPE scalar_binary_op(ETYPE s, ETYPE t);
626 * EVector a = ...;
627 * VectorSpecies<E> species = a.species();
628 * EVector b = ...;
629 * b.check(species); // must have same species
630 * VectorMask<E> m = ...;
631 * m.check(species); // must have same species
632 * boolean[] ar = new boolean[a.length()];
633 * for (int i = 0; i < ar.length; i++) {
634 * if (m.laneIsSet(i)) {
635 * ar[i] = scalar_binary_op(a.lane(i), b.lane(i));
636 * } else {
637 * ar[i] = a.lane(i); // from first input
638 * }
639 * }
640 * EVector r = EVector.fromArray(species, ar, 0);
641 * }</pre>
642 *
643 * <h2><a id="lane-order"></a>Lane order and byte order</h2>
644 *
645 * The number of lane values stored in a given vector is referred to
646 * as its {@linkplain #length() vector length} or {@code VLENGTH}.
647 *
648 * It is useful to consider vector lanes as ordered
649 * <em>sequentially</em> from first to last, with the first lane
650 * numbered {@code 0}, the next lane numbered {@code 1}, and so on to
651 * the last lane numbered {@code VLENGTH-1}. This is a temporal
652 * order, where lower-numbered lanes are considered earlier than
653 * higher-numbered (later) lanes. This API uses these terms
654 * in preference to spatial terms such as "left", "right", "high",
655 * and "low".
656 *
657 * <p> Temporal terminology works well for vectors because they
658 * (usually) represent small fixed-sized segments in a long sequence
659 * of workload elements, where the workload is conceptually traversed
660 * in time order from beginning to end. (This is a mental model: it
661 * does not exclude multicore divide-and-conquer techniques.) Thus,
662 * when a scalar loop is transformed into a vector loop, adjacent
663 * scalar items (one earlier, one later) in the workload end up as
664 * adjacent lanes in a single vector (again, one earlier, one later).
665 * At a vector boundary, the last lane item in the earlier vector is
666 * adjacent to (and just before) the first lane item in the
667 * immediately following vector.
668 *
669 * <p> Vectors are also sometimes thought of in spatial terms, where
670 * the first lane is placed at an edge of some virtual paper, and
671 * subsequent lanes are presented in order next to it. When using
672 * spatial terms, all directions are equally plausible: Some vector
673 * notations present lanes from left to right, and others from right
674 * to left; still others present from top to bottom or vice versa.
675 * Using the language of time (before, after, first, last) instead of
676 * space (left, right, high, low) is often more likely to avoid
677 * misunderstandings.
678 *
679 * <p> As second reason to prefer temporal to spatial language about
680 * vector lanes is the fact that the terms "left", "right", "high" and
681 * "low" are widely used to describe the relations between bits in
682 * scalar values. The leftmost or highest bit in a given type is
683 * likely to be a sign bit, while the rightmost or lowest bit is
684 * likely to be the arithmetically least significant, and so on.
685 * Applying these terms to vector lanes risks confusion, however,
686 * because it is relatively rare to find algorithms where, given two
687 * adjacent vector lanes, one lane is somehow more arithmetically
688 * significant than its neighbor, and even in those cases, there is no
689 * general way to know which neighbor is the the more significant.
690 *
691 * <p> Putting the terms together, we view the information structure
692 * of a vector as a temporal sequence of lanes ("first", "next",
693 * "earlier", "later", "last", etc.) of bit-strings which are
694 * internally ordered spatially (either "low" to "high" or "right" to
695 * "left"). The primitive values in the lanes are decoded from these
696 * bit-strings, in the usual way. Most vector operations, like most
697 * Java scalar operators, treat primitive values as atomic values, but
698 * some operations reveal the internal bit-string structure.
699 *
700 * <p> When a vector is loaded from or stored into memory, the order
701 * of vector lanes is <em>always consistent </em> with the inherent
702 * ordering of the memory container. This is true whether or not
703 * individual lane elements are subject to "byte swapping" due to
704 * details of byte order. Thus, while the scalar lane elements of
705 * vector might be "byte swapped", the lanes themselves are never
706 * reordered, except by an explicit method call that performs
707 * cross-lane reordering.
708 *
709 * <p> When vector lane values are stored to Java variables of the
710 * same type, byte swapping is performed if and only if the
711 * implementation of the vector hardware requires such swapping. It
712 * is therefore unconditional and invisible.
713 *
714 * <p> As a useful fiction, this API presents a consistent illusion
715 * that vector lane bytes are composed into larger lane scalars in
716 * <em>little endian order</em>. This means that storing a vector
717 * into a Java byte array will reveal the successive bytes of the
718 * vector lane values in little-endian order on all platforms,
719 * regardless of native memory order, and also regardless of byte
720 * order (if any) within vector unit registers.
721 *
722 * <p> This hypothetical little-endian ordering also appears when a
723 * {@linkplain #reinterpretShape(VectorSpecies,int) reinterpretation cast} is
724 * applied in such a way that lane boundaries are discarded and
725 * redrawn differently, while maintaining vector bits unchanged. In
726 * such an operation, two adjacent lanes will contribute bytes to a
727 * single new lane (or vice versa), and the sequential order of the
728 * two lanes will determine the arithmetic order of the bytes in the
729 * single lane. In this case, the little-endian convention provides
730 * portable results, so that on all platforms earlier lanes tend to
731 * contribute lower (rightward) bits, and later lanes tend to
732 * contribute higher (leftward) bits. The {@linkplain #reinterpretAsBytes()
733 * reinterpretation casts} between {@link ByteVector}s and the
734 * other non-byte vectors use this convention to clarify their
735 * portable semantics.
736 *
737 * <p> The little-endian fiction for relating lane order to per-lane
738 * byte order is slightly preferable to an equivalent big-endian
739 * fiction, because some related formulas are much simpler,
740 * specifically those which renumber bytes after lane structure
741 * changes. The earliest byte is invariantly earliest across all lane
742 * structure changes, but only if little-endian convention are used.
743 * The root cause of this is that bytes in scalars are numbered from
744 * the least significant (rightmost) to the most significant
745 * (leftmost), and almost never vice-versa. If we habitually numbered
746 * sign bits as zero (as on some computers) then this API would reach
747 * for big-endian fictions to create unified addressing of vector
748 * bytes.
749 *
750 * <h2><a id="memory"></a>Memory operations</h2>
751 *
752 * As was already mentioned, vectors can be loaded from memory and
753 * stored back. An optional mask can control which individual memory
754 * locations are read from or written to. The shape of a vector
755 * determines how much memory it will occupy.
756 *
757 * An implementation typically has the property, in the absence of
758 * masking, that lanes are stored as a dense sequence of back-to-back
759 * values in memory, the same as a dense (gap-free) series of single
760 * scalar values in an array of the scalar type.
761 *
762 * In such cases memory order corresponds exactly to lane order. The
763 * first vector lane value occupies the first position in memory, and so on,
764 * up to the length of the vector. Further, the memory order of stored
765 * vector lanes corresponds to increasing index values in a Java array or
766 * in a {@link java.nio.ByteBuffer}.
767 *
768 * <p> Byte order for lane storage is chosen such that the stored
769 * vector values can be read or written as single primitive values,
770 * within the array or buffer that holds the vector, producing the
771 * same values as the lane-wise values within the vector.
772 * This fact is independent of the convenient fiction that lane values
773 * inside of vectors are stored in little-endian order.
774 *
775 * <p> For example,
776 * {@link FloatVector#fromArray(VectorSpecies, float[], int)
777 * FloatVector.fromArray(fsp,fa,i)}
778 * creates and returns a float vector of some particular species {@code fsp},
779 * with elements loaded from some float array {@code fa}.
780 * The first lane is loaded from {@code fa[i]} and the last lane
781 * is initialized loaded from {@code fa[i+VL-1]}, where {@code VL}
782 * is the length of the vector as derived from the species {@code fsp}.
783 * Then, {@link FloatVector#add(Vector) fv=fv.add(fv2)}
784 * will produce another float vector of that species {@code fsp},
785 * given a vector {@code fv2} of the same species {@code fsp}.
786 * Next, {@link FloatVector#compare(VectorOperators.Comparison,float)
787 * mnz=fv.compare(NE, 0.0f)} tests whether the result is zero,
788 * yielding a mask {@code mnz}. The non-zero lanes (and only those
789 * lanes) can then be stored back into the original array elements
790 * using the statement
791 * {@link FloatVector#intoArray(float[],int,VectorMask) fv.intoArray(fa,i,mnz)}.
792 *
793 * <h2><a id="expansion"></a>Expansions, contractions, and partial results</h2>
794 *
795 * Since vectors are fixed in size, occasions often arise where the
796 * logical result of an operation is not the same as the physical size
797 * of the proposed output vector. To encourage user code that is as
798 * portable and predictable as possible, this API has a systematic
799 * approach to the design of such <em>resizing</em> vector operations.
800 *
801 * <p> As a basic principle, lane-wise operations are
802 * <em>length-invariant</em>, unless clearly marked otherwise.
803 * Length-invariance simply means that
804 * if {@code VLENGTH} lanes go into an operation, the same number
805 * of lanes come out, with nothing discarded and no extra padding.
806 *
807 * <p> As a second principle, sometimes in tension with the first,
808 * lane-wise operations are also <em>shape-invariant</em>, unless
809 * clearly marked otherwise.
810 *
811 * Shape-invariance means that {@code VSHAPE} is constant for typical
812 * computations. Keeping the same shape throughout a computation
813 * helps ensure that scarce vector resources are efficiently used.
814 * (On some hardware platforms shape changes could cause unwanted
815 * effects like extra data movement instructions, round trips through
816 * memory, or pipeline bubbles.)
817 *
818 * <p> Tension between these principles arises when an operation
819 * produces a <em>logical result</em> that is too large for the
820 * required output {@code VSHAPE}. In other cases, when a logical
821 * result is smaller than the capacity of the output {@code VSHAPE},
822 * the positioning of the logical result is open to question, since
823 * the physical output vector must contain a mix of logical result and
824 * padding.
825 *
826 * <p> In the first case, of a too-large logical result being crammed
827 * into a too-small output {@code VSHAPE}, we say that data has
828 * <em>expanded</em>. In other words, an <em>expansion operation</em>
829 * has caused the output shape to overflow. Symmetrically, in the
830 * second case of a small logical result fitting into a roomy output
831 * {@code VSHAPE}, the data has <em>contracted</em>, and the
832 * <em>contraction operation</em> has required the output shape to pad
833 * itself with extra zero lanes.
834 *
835 * <p> In both cases we can speak of a parameter {@code M} which
836 * measures the <em>expansion ratio</em> or <em>contraction ratio</em>
837 * between the logical result size (in bits) and the bit-size of the
838 * actual output shape. When vector shapes are changed, and lane
839 * sizes are not, {@code M} is just the integral ratio of the output
840 * shape to the logical result. (With the possible exception of
841 * the {@linkplain VectorShape#S_Max_BIT maximum shape}, all vector
842 * sizes are powers of two, and so the ratio {@code M} is always
843 * an integer. In the hypothetical case of a non-integral ratio,
844 * the value {@code M} would be rounded up to the next integer,
845 * and then the same general considerations would apply.)
846 *
847 * <p> If the logical result is larger than the physical output shape,
848 * such a shape change must inevitably drop result lanes (all but
849 * {@code 1/M} of the logical result). If the logical size is smaller
850 * than the output, the shape change must introduce zero-filled lanes
851 * of padding (all but {@code 1/M} of the physical output). The first
852 * case, with dropped lanes, is an expansion, while the second, with
853 * padding lanes added, is a contraction.
854 *
855 * <p> Similarly, consider a lane-wise conversion operation which
856 * leaves the shape invariant but changes the lane size by a ratio of
857 * {@code M}. If the logical result is larger than the output (or
858 * input), this conversion must reduce the {@code VLENGTH} lanes of the
859 * output by {@code M}, dropping all but {@code 1/M} of the logical
860 * result lanes. As before, the dropping of lanes is the hallmark of
861 * an expansion. A lane-wise operation which contracts lane size by a
862 * ratio of {@code M} must increase the {@code VLENGTH} by the same
863 * factor {@code M}, filling the extra lanes with a zero padding
864 * value; because padding must be added this is a contraction.
865 *
866 * <p> It is also possible (though somewhat confusing) to change both
867 * lane size and container size in one operation which performs both
868 * lane conversion <em>and</em> reshaping. If this is done, the same
869 * rules apply, but the logical result size is the product of the
870 * input size times any expansion or contraction ratio from the lane
871 * change size.
872 *
873 * <p> For completeness, we can also speak of <em>in-place
874 * operations</em> for the frequent case when resizing does not occur.
875 * With an in-place operation, the data is simply copied from logical
876 * output to its physical container with no truncation or padding.
877 * The ratio parameter {@code M} in this case is unity.
878 *
879 * <p> Note that the classification of contraction vs. expansion
880 * depends on the relative sizes of the logical result and the
881 * physical output container. The size of the input container may be
882 * larger or smaller than either of the other two values, without
883 * changing the classification. For example, a conversion from a
884 * 128-bit shape to a 256-bit shape will be a contraction in many
885 * cases, but it would be an expansion if it were combined with a
886 * conversion from {@code byte} to {@code long}, since in that case
887 * the logical result would be 1024 bits in size. This example also
888 * illustrates that a logical result does not need to correspond to
889 * any particular platform-supported vector shape.
890 *
891 * <p> Although lane-wise masked operations can be viewed as producing
892 * partial operations, they are not classified (in this API) as
893 * expansions or contractions. A masked load from an array surely
894 * produces a partial vector, but there is no meaningful "logical
895 * output vector" that this partial result was contracted from.
896 *
897 * <p> Some care is required with these terms, because it is the
898 * <em>data</em>, not the <em>container size</em>, that is expanding
899 * or contracting, relative to the size of its output container.
900 * Thus, resizing a 128-bit input into 512-bit vector has the effect
901 * of a <em>contraction</em>. Though the 128 bits of payload hasn't
902 * changed in size, we can say it "looks smaller" in its new 512-bit
903 * home, and this will capture the practical details of the situation.
904 *
905 * <p> If a vector method might expand its data, it accepts an extra
906 * {@code int} parameter called {@code part}, or the "part number".
907 * The part number must be in the range {@code [0..M-1]}, where
908 * {@code M} is the expansion ratio. The part number selects one
909 * of {@code M} contiguous disjoint equally-sized blocks of lanes
910 * from the logical result and fills the physical output vector
911 * with this block of lanes.
912 *
913 * <p> Specifically, the lanes selected from the logical result of an
914 * expansion are numbered in the range {@code [R..R+L-1]}, where
915 * {@code L} is the {@code VLENGTH} of the physical output vector, and
916 * the origin of the block, {@code R}, is {@code part*L}.
917 *
918 * <p> A similar convention applies to any vector method that might
919 * contract its data. Such a method also accepts an extra part number
920 * parameter (again called {@code part}) which steers the contracted
921 * data lanes one of {@code M} contiguous disjoint equally-sized
922 * blocks of lanes in the physical output vector. The remaining lanes
923 * are filled with zero, or as specified by the method.
924 *
925 * <p> Specifically, the data is steered into the lanes numbered in the
926 * range {@code [R..R+L-1]}, where {@code L} is the {@code VLENGTH} of
927 * the logical result vector, and the origin of the block, {@code R},
928 * is again a multiple of {@code L} selected by the part number,
929 * specifically {@code |part|*L}.
930 *
931 * <p> In the case of a contraction, the part number must be in the
932 * non-positive range {@code [-M+1..0]}. This convention is adopted
933 * because some methods can perform both expansions and contractions,
934 * in a data-dependent manner, and the extra sign on the part number
935 * serves as an error check. If vector method takes a part number and
936 * is invoked to perform an in-place operation (neither contracting
937 * nor expanding), the {@code part} parameter must be exactly zero.
938 * Part numbers outside the allowed ranges will elicit an indexing
939 * exception. Note that in all cases a zero part number is valid, and
940 * corresponds to an operation which preserves as many lanes as
941 * possible from the beginning of the logical result, and places them
942 * into the beginning of the physical output container. This is
943 * often a desirable default, so a part number of zero is safe
944 * in all cases and useful in most cases.
945 *
946 * <p> The various resizing operations of this API contract or expand
947 * their data as follows:
948 * <ul>
949 *
950 * <li>
951 * {@link Vector#convert(VectorOperators.Conversion,int) Vector.convert()}
952 * will expand (respectively, contract) its operand by ratio
953 * {@code M} if the
954 * {@linkplain #elementSize() element size} of its output is
955 * larger (respectively, smaller) by a factor of {@code M}.
956 * If the element sizes of input and output are the same,
957 * then {@code convert()} is an in-place operation.
958 *
959 * <li>
960 * {@link Vector#convertShape(VectorOperators.Conversion,VectorSpecies,int) Vector.convertShape()}
961 * will expand (respectively, contract) its operand by ratio
962 * {@code M} if the bit-size of its logical result is
963 * larger (respectively, smaller) than the bit-size of its
964 * output shape.
965 * The size of the logical result is defined as the
966 * {@linkplain #elementSize() element size} of the output,
967 * times the {@code VLENGTH} of its input.
968 *
969 * Depending on the ratio of the changed lane sizes, the logical size
970 * may be (in various cases) either larger or smaller than the input
971 * vector, independently of whether the operation is an expansion
972 * or contraction.
973 *
974 * <li>
975 * Since {@link Vector#castShape(VectorSpecies,int) Vector.castShape()}
976 * is a convenience method for {@code convertShape()}, its classification
977 * as an expansion or contraction is the same as for {@code convertShape()}.
978 *
979 * <li>
980 * {@link Vector#reinterpretShape(VectorSpecies,int) Vector.reinterpretShape()}
981 * is an expansion (respectively, contraction) by ratio {@code M} if the
982 * {@linkplain #bitSize() vector bit-size} of its input is
983 * crammed into a smaller (respectively, dropped into a larger)
984 * output container by a factor of {@code M}.
985 * Otherwise it is an in-place operation.
986 *
987 * Since this method is a reinterpretation cast that can erase and
988 * redraw lane boundaries as well as modify shape, the input vector's
989 * lane size and lane count are irrelevant to its classification as
990 * expanding or contracting.
991 *
992 * <li>
993 * The {@link #unslice(int,Vector,int) unslice()} methods expand
994 * by a ratio of {@code M=2}, because the single input slice is
995 * positioned and inserted somewhere within two consecutive background
996 * vectors. The part number selects the first or second background
997 * vector, as updated by the inserted slice.
998 * Note that the corresponding
999 * {@link #slice(int,Vector) slice()} methods, although inverse
1000 * to the {@code unslice()} methods, do not contract their data
1001 * and thus require no part number. This is because
1002 * {@code slice()} delivers a slice of exactly {@code VLENGTH}
1003 * lanes extracted from two input vectors.
1004 * </ul>
1005 *
1006 * The method {@link VectorSpecies#partLimit(VectorSpecies,boolean)
1007 * partLimit()} on {@link VectorSpecies} can be used, before any
1008 * expanding or contracting operation is performed, to query the
1009 * limiting value on a part parameter for a proposed expansion
1010 * or contraction. The value returned from {@code partLimit()} is
1011 * positive for expansions, negative for contractions, and zero for
1012 * in-place operations. Its absolute value is the parameter {@code
1013 * M}, and so it serves as an exclusive limit on valid part number
1014 * arguments for the relevant methods. Thus, for expansions, the
1015 * {@code partLimit()} value {@code M} is the exclusive upper limit
1016 * for part numbers, while for contractions the {@code partLimit()}
1017 * value {@code -M} is the exclusive <em>lower</em> limit.
1018 *
1019 * <h2><a id="cross-lane"></a>Moving data across lane boundaries</h2>
1020 * The cross-lane methods which do not redraw lanes or change species
1021 * are more regularly structured and easier to reason about.
1022 * These operations are:
1023 * <ul>
1024 *
1025 * <li>The {@link #slice(int,Vector) slice()} family of methods,
1026 * which extract contiguous slice of {@code VLENGTH} fields from
1027 * a given origin point within a concatenated pair of vectors.
1028 *
1029 * <li>The {@link #unslice(int,Vector,int) unslice()} family of
1030 * methods, which insert a contiguous slice of {@code VLENGTH} fields
1031 * into a concatenated pair of vectors at a given origin point.
1032 *
1033 * <li>The {@link #rearrange(VectorShuffle) rearrange()} family of
1034 * methods, which select an arbitrary set of {@code VLENGTH} lanes
1035 * from one or two input vectors, and assemble them in an arbitrary
1036 * order. The selection and order of lanes is controlled by a
1037 * {@code VectorShuffle} object, which acts as an routing table
1038 * mapping source lanes to destination lanes. A {@code VectorShuffle}
1039 * can encode a mathematical permutation as well as many other
1040 * patterns of data movement.
1041 *
1042 * </ul>
1043 * <p> Some vector operations are not lane-wise, but rather move data
1044 * across lane boundaries. Such operations are typically rare in SIMD
1045 * code, though they are sometimes necessary for specific algorithms
1046 * that manipulate data formats at a low level, and/or require SIMD
1047 * data to move in complex local patterns. (Local movement in a small
1048 * window of a large array of data is relatively unusual, although
1049 * some highly patterned algorithms call for it.) In this API such
1050 * methods are always clearly recognizable, so that simpler lane-wise
1051 * reasoning can be confidently applied to the rest of the code.
1052 *
1053 * <p> In some cases, vector lane boundaries are discarded and
1054 * "redrawn from scratch", so that data in a given input lane might
1055 * appear (in several parts) distributed through several output lanes,
1056 * or (conversely) data from several input lanes might be consolidated
1057 * into a single output lane. The fundamental method which can redraw
1058 * lanes boundaries is
1059 * {@link #reinterpretShape(VectorSpecies,int) reinterpretShape()}.
1060 * Built on top of this method, certain convenience methods such
1061 * as {@link #reinterpretAsBytes() reinterpretAsBytes()} or
1062 * {@link #reinterpretAsInts() reinterpretAsInts()} will
1063 * (potentially) redraw lane boundaries, while retaining the
1064 * same overall vector shape.
1065 *
1066 * <p> Operations which produce or consume a scalar result can be
1067 * viewed as very simple cross-lane operations. Methods in the
1068 * {@link #reduceLanesToLong(VectorOperators.Associative)
1069 * reduceLanes()} family fold together all lanes (or mask-selected
1070 * lanes) of a method and return a single result. As an inverse, the
1071 * {@link #broadcast(long) broadcast} family of methods can be thought
1072 * of as crossing lanes in the other direction, from a scalar to all
1073 * lanes of the output vector. Single-lane access methods such as
1074 * {@code lane(I)} or {@code withLane(I,E)} might also be regarded as
1075 * very simple cross-lane operations.
1076 *
1077 * <p> Likewise, a method which moves a non-byte vector to or from a
1078 * byte array could be viewed as a cross-lane operation, because the
1079 * vector lanes must be distributed into separate bytes, or (in the
1080 * other direction) consolidated from array bytes.
1081 *
1082 * @implNote
1083 *
1084 * <h2>Hardware platform dependencies and limitations</h2>
1085 *
1086 * The Vector API is to accelerate computations in style of Single
1087 * Instruction Multiple Data (SIMD), using available hardware
1088 * resources such as vector hardware registers and vector hardware
1089 * instructions. The API is designed to make effective use of
1090 * multiple SIMD hardware platforms.
1091 *
1092 * <p> This API will also work correctly even on Java platforms which
1093 * do not include specialized hardware support for SIMD computations.
1094 * The Vector API is not likely to provide any special performance
1095 * benefit on such platforms.
1096 *
1097 * <p> Currently the implementation is optimized to work best on:
1098 *
1099 * <ul>
1100 *
1101 * <li> Intel x64 platforms supporting at least AVX2 up to AVX-512.
1102 * Masking using mask registers and mask accepting hardware
1103 * instructions on AVX-512 are not currently supported.
1104 *
1105 * <li> ARM AArch64 platforms supporting NEON. Although the API has
1106 * been designed to ensure ARM SVE instructions can be supported
1107 * (vector sizes between 128 to 2048 bits) there is currently no
1108 * implementation of such instructions and the general masking
1109 * capability.
1110 *
1111 * </ul>
1112 * The implementation currently supports masked lane-wise operations
1113 * in a cross-platform manner by composing the unmasked lane-wise
1114 * operation with {@link #blend(Vector, VectorMask) blend} as in
1115 * the expression {@code a.blend(a.lanewise(op, b), m)}, where
1116 * {@code a} and {@code b} are vectors, {@code op} is the vector
1117 * operation, and {@code m} is the mask.
1118 *
1119 * <p> The implementation does not currently support optimal
1120 * vectorized instructions for floating point transcendental
1121 * functions (such as operators {@link VectorOperators#SIN SIN}
1122 * and {@link VectorOperators#LOG LOG}).
1123 *
1124 * <h2>No boxing of primitives</h2>
1125 *
1126 * Although a vector type like {@code Vector<Integer>} may seem to
1127 * work with boxed {@code Integer} values, the overheads associated
1128 * with boxing are avoided by having each vector subtype work
1129 * internally on lane values of the actual {@code ETYPE}, such as
1130 * {@code int}.
1131 *
1132 * <h2>Value-based classes and identity operations</h2>
1133 *
1134 * {@code Vector}, along with all of its subtypes and many of its
1135 * helper types like {@code VectorMask} and {@code VectorShuffle}, is a
1136 * <a href="{@docRoot}/java.base/java/lang/doc-files/ValueBased.html">value-based</a>
1137 * class.
1138 *
1139 * <p> Once created, a vector is never mutated, not even if only
1140 * {@linkplain IntVector#withLane(int,int) a single lane is changed}.
1141 * A new vector is always created to hold a new configuration
1142 * of lane values. The unavailability of mutative methods is a
1143 * necessary consequence of suppressing the object identity of
1144 * all vectors, as value-based classes.
1145 *
1146 * <p> With {@code Vector},
1147 *
1148 * <!-- The following paragraph is shared verbatim
1149 * -- between Vector.java and package-info.java -->
1150 * identity-sensitive operations such as {@code ==} may yield
1151 * unpredictable results, or reduced performance. Oddly enough,
1152 * {@link Vector#equals(Object) v.equals(w)} is likely to be faster
1153 * than {@code v==w}, since {@code equals} is <em>not</em> an identity
1154 * sensitive method.
1155 *
1156 * Also, these objects can be stored in locals and parameters and as
1157 * {@code static final} constants, but storing them in other Java
1158 * fields or in array elements, while semantically valid, may incur
1159 * performance penalties.
1160 * <!-- The preceding paragraph is shared verbatim
1161 * -- between Vector.java and package-info.java -->
1162 *
1163 * @param <E> the boxed version of {@code ETYPE},
1164 * the element type of a vector
1165 *
1166 */
1167 @SuppressWarnings("exports")
1168 public abstract class Vector<E> extends jdk.internal.vm.vector.VectorSupport.Vector<E> {
1169
1170 // This type is sealed within its package.
1171 // Users cannot roll their own vector types.
1172 Vector(Object bits) {
1173 super(bits);
1174 }
1175
1176 /**
1177 * Returns the species of this vector.
1178 *
1179 * @return the species of this vector
1180 */
1181 public abstract VectorSpecies<E> species();
1182
1183 /**
1184 * Returns the primitive <a href="Vector.html#ETYPE">element type</a>
1185 * ({@code ETYPE}) of this vector.
1186 *
1187 * @implSpec
1188 * This is the same value as {@code this.species().elementType()}.
1189 *
1190 * @return the primitive element type of this vector
1191 */
1192 public abstract Class<E> elementType();
1193
1194 /**
1195 * Returns the size of each lane, in bits, of this vector.
1196 *
1197 * @implSpec
1198 * This is the same value as {@code this.species().elementSize()}.
1199 *
1200 * @return the lane size, in bits, of this vector
1201 */
1202 public abstract int elementSize();
1203
1204 /**
1205 * Returns the shape of this vector.
1206 *
1207 * @implSpec
1208 * This is the same value as {@code this.species().vectorShape()}.
1209 *
1210 * @return the shape of this vector
1211 */
1212 public abstract VectorShape shape();
1213
1214 /**
1215 * Returns the lane count, or <a href="Vector.html#VLENGTH">vector length</a>
1216 * ({@code VLENGTH}).
1217 *
1218 * @return the lane count
1219 */
1220 public abstract int length();
1221
1222 /**
1223 * Returns the total size, in bits, of this vector.
1224 *
1225 * @implSpec
1226 * This is the same value as {@code this.shape().vectorBitSize()}.
1227 *
1228 * @return the total size, in bits, of this vector
1229 */
1230 public abstract int bitSize();
1231
1232 /**
1233 * Returns the total size, in bytes, of this vector.
1234 *
1235 * @implSpec
1236 * This is the same value as {@code this.bitSize()/Byte.SIZE}.
1237 *
1238 * @return the total size, in bytes, of this vector
1239 */
1240 public abstract int byteSize();
1241
1242 /// Arithmetic
1243
1244 /**
1245 * Operates on the lane values of this vector.
1246 *
1247 * This is a <a href="Vector.html#lane-wise">lane-wise</a>
1248 * unary operation which applies
1249 * the selected operation to each lane.
1250 *
1251 * @apiNote
1252 * Subtypes improve on this method by sharpening
1253 * the method return type.
1254 *
1255 * @param op the operation used to process lane values
1256 * @return the result of applying the operation lane-wise
1257 * to the input vector
1258 * @throws UnsupportedOperationException if this vector does
1259 * not support the requested operation
1260 * @see VectorOperators#NEG
1261 * @see VectorOperators#NOT
1262 * @see VectorOperators#SIN
1263 * @see #lanewise(VectorOperators.Unary,VectorMask)
1264 * @see #lanewise(VectorOperators.Binary,Vector)
1265 * @see #lanewise(VectorOperators.Ternary,Vector,Vector)
1266 */
1267 public abstract Vector<E> lanewise(VectorOperators.Unary op);
1268
1269 /**
1270 * Operates on the lane values of this vector,
1271 * with selection of lane elements controlled by a mask.
1272 *
1273 * This is a lane-wise unary operation which applies
1274 * the selected operation to each lane.
1275 *
1276 * @apiNote
1277 * Subtypes improve on this method by sharpening
1278 * the method return type.
1279 *
1280 * @param op the operation used to process lane values
1281 * @param m the mask controlling lane selection
1282 * @return the result of applying the operation lane-wise
1283 * to the input vector
1284 * @throws UnsupportedOperationException if this vector does
1285 * not support the requested operation
1286 * @see #lanewise(VectorOperators.Unary)
1287 */
1288 public abstract Vector<E> lanewise(VectorOperators.Unary op,
1289 VectorMask<E> m);
1290
1291 /**
1292 * Combines the corresponding lane values of this vector
1293 * with those of a second input vector.
1294 *
1295 * This is a <a href="Vector.html#lane-wise">lane-wise</a>
1296 * binary operation which applies
1297 * the selected operation to each lane.
1298 *
1299 * @apiNote
1300 * Subtypes improve on this method by sharpening
1301 * the method return type.
1302 *
1303 * @param op the operation used to combine lane values
1304 * @param v the input vector
1305 * @return the result of applying the operation lane-wise
1306 * to the two input vectors
1307 * @throws UnsupportedOperationException if this vector does
1308 * not support the requested operation
1309 * @see VectorOperators#ADD
1310 * @see VectorOperators#XOR
1311 * @see VectorOperators#ATAN2
1312 * @see #lanewise(VectorOperators.Binary,Vector,VectorMask)
1313 * @see #lanewise(VectorOperators.Unary)
1314 * @see #lanewise(VectorOperators.Ternary,Vector, Vector)
1315 */
1316 public abstract Vector<E> lanewise(VectorOperators.Binary op,
1317 Vector<E> v);
1318
1319 /**
1320 * Combines the corresponding lane values of this vector
1321 * with those of a second input vector,
1322 * with selection of lane elements controlled by a mask.
1323 *
1324 * This is a lane-wise binary operation which applies
1325 * the selected operation to each lane.
1326 *
1327 * @apiNote
1328 * Subtypes improve on this method by sharpening
1329 * the method return type.
1330 *
1331 * @param op the operation used to combine lane values
1332 * @param v the second input vector
1333 * @param m the mask controlling lane selection
1334 * @return the result of applying the operation lane-wise
1335 * to the two input vectors
1336 * @throws UnsupportedOperationException if this vector does
1337 * not support the requested operation
1338 * @see #lanewise(VectorOperators.Binary,Vector)
1339 */
1340 public abstract Vector<E> lanewise(VectorOperators.Binary op,
1341 Vector<E> v, VectorMask<E> m);
1342
1343 /**
1344 * Combines the lane values of this vector
1345 * with the value of a broadcast scalar.
1346 *
1347 * This is a lane-wise binary operation which applies
1348 * the selected operation to each lane.
1349 * The return value will be equal to this expression:
1350 * {@code this.lanewise(op, this.broadcast(e))}.
1351 *
1352 * @apiNote
1353 * The {@code long} value {@code e} must be accurately
1354 * representable by the {@code ETYPE} of this vector's species,
1355 * so that {@code e==(long)(ETYPE)e}. This rule is enforced
1356 * by the implicit call to {@code broadcast()}.
1357 * <p>
1358 * Subtypes improve on this method by sharpening
1359 * the method return type and
1360 * the type of the scalar parameter {@code e}.
1361 *
1362 * @param op the operation used to combine lane values
1363 * @param e the input scalar
1364 * @return the result of applying the operation lane-wise
1365 * to the input vector and the scalar
1366 * @throws UnsupportedOperationException if this vector does
1367 * not support the requested operation
1368 * @throws IllegalArgumentException
1369 * if the given {@code long} value cannot
1370 * be represented by the right operand type
1371 * of the vector operation
1372 * @see #broadcast(long)
1373 * @see #lanewise(VectorOperators.Binary,long,VectorMask)
1374 */
1375 public abstract Vector<E> lanewise(VectorOperators.Binary op,
1376 long e);
1377
1378 /**
1379 * Combines the corresponding lane values of this vector
1380 * with those of a second input vector,
1381 * with selection of lane elements controlled by a mask.
1382 *
1383 * This is a lane-wise binary operation which applies
1384 * the selected operation to each lane.
1385 * The second operand is a broadcast integral value.
1386 * The return value will be equal to this expression:
1387 * {@code this.lanewise(op, this.broadcast(e), m)}.
1388 *
1389 * @apiNote
1390 * The {@code long} value {@code e} must be accurately
1391 * representable by the {@code ETYPE} of this vector's species,
1392 * so that {@code e==(long)(ETYPE)e}. This rule is enforced
1393 * by the implicit call to {@code broadcast()}.
1394 * <p>
1395 * Subtypes improve on this method by sharpening
1396 * the method return type and
1397 * the type of the scalar parameter {@code e}.
1398 *
1399 * @param op the operation used to combine lane values
1400 * @param e the input scalar
1401 * @param m the mask controlling lane selection
1402 * @return the result of applying the operation lane-wise
1403 * to the input vector and the scalar
1404 * @throws UnsupportedOperationException if this vector does
1405 * not support the requested operation
1406 * @throws IllegalArgumentException
1407 * if the given {@code long} value cannot
1408 * be represented by the right operand type
1409 * of the vector operation
1410 * @see #broadcast(long)
1411 * @see #lanewise(VectorOperators.Binary,Vector,VectorMask)
1412 */
1413 public abstract Vector<E> lanewise(VectorOperators.Binary op,
1414 long e, VectorMask<E> m);
1415
1416 /**
1417 * Combines the corresponding lane values of this vector
1418 * with the lanes of a second and a third input vector.
1419 *
1420 * This is a <a href="Vector.html#lane-wise">lane-wise</a>
1421 * ternary operation which applies
1422 * the selected operation to each lane.
1423 *
1424 * @apiNote
1425 * Subtypes improve on this method by sharpening
1426 * the method return type.
1427 *
1428 * @param op the operation used to combine lane values
1429 * @param v1 the second input vector
1430 * @param v2 the third input vector
1431 * @return the result of applying the operation lane-wise
1432 * to the three input vectors
1433 * @throws UnsupportedOperationException if this vector does
1434 * not support the requested operation
1435 * @see VectorOperators#BITWISE_BLEND
1436 * @see VectorOperators#FMA
1437 * @see #lanewise(VectorOperators.Unary)
1438 * @see #lanewise(VectorOperators.Binary,Vector)
1439 * @see #lanewise(VectorOperators.Ternary,Vector,Vector,VectorMask)
1440 */
1441 public abstract Vector<E> lanewise(VectorOperators.Ternary op,
1442 Vector<E> v1,
1443 Vector<E> v2);
1444
1445 /**
1446 * Combines the corresponding lane values of this vector
1447 * with the lanes of a second and a third input vector,
1448 * with selection of lane elements controlled by a mask.
1449 *
1450 * This is a lane-wise ternary operation which applies
1451 * the selected operation to each lane.
1452 *
1453 * @apiNote
1454 * Subtypes improve on this method by sharpening
1455 * the method return type.
1456 *
1457 * @param op the operation used to combine lane values
1458 * @param v1 the second input vector
1459 * @param v2 the third input vector
1460 * @param m the mask controlling lane selection
1461 * @return the result of applying the operation lane-wise
1462 * to the three input vectors
1463 * @throws UnsupportedOperationException if this vector does
1464 * not support the requested operation
1465 * @see #lanewise(VectorOperators.Ternary,Vector,Vector)
1466 */
1467 public abstract Vector<E> lanewise(VectorOperators.Ternary op,
1468 Vector<E> v1, Vector<E> v2,
1469 VectorMask<E> m);
1470
1471 // Note: lanewise(Binary) has two rudimentary broadcast
1472 // operations from an approximate scalar type (long).
1473 // We do both with that, here, for lanewise(Ternary).
1474 // The vector subtypes supply a full suite of
1475 // broadcasting and masked lanewise operations
1476 // for their specific ETYPEs:
1477 // lanewise(Unary, [mask])
1478 // lanewise(Binary, [e | v], [mask])
1479 // lanewise(Ternary, [e1 | v1], [e2 | v2], [mask])
1480
1481 /// Full-service binary ops: ADD, SUB, MUL, DIV
1482
1483 // Full-service functions support all four variations
1484 // of vector vs. broadcast scalar, and mask vs. not.
1485 // The lanewise generic operator is (by this definition)
1486 // also a full-service function.
1487
1488 // Other named functions handle just the one named
1489 // variation. Most lanewise operations are *not* named,
1490 // and are reached only by lanewise.
1491
1492 /**
1493 * Adds this vector to a second input vector.
1494 *
1495 * This is a lane-wise binary operation which applies
1496 * the primitive addition operation ({@code +})
1497 * to each pair of corresponding lane values.
1498 *
1499 * This method is also equivalent to the expression
1500 * {@link #lanewise(VectorOperators.Binary,Vector)
1501 * lanewise}{@code (}{@link VectorOperators#ADD
1502 * ADD}{@code , v)}.
1503 *
1504 * <p>
1505 * As a full-service named operation, this method
1506 * comes in masked and unmasked overloadings, and
1507 * (in subclasses) also comes in scalar-broadcast
1508 * overloadings (both masked and unmasked).
1509 *
1510 * @param v a second input vector
1511 * @return the result of adding this vector to the second input vector
1512 * @see #add(Vector,VectorMask)
1513 * @see IntVector#add(int)
1514 * @see VectorOperators#ADD
1515 * @see #lanewise(VectorOperators.Binary,Vector)
1516 * @see IntVector#lanewise(VectorOperators.Binary,int)
1517 */
1518 public abstract Vector<E> add(Vector<E> v);
1519
1520 /**
1521 * Adds this vector to a second input vector, selecting lanes
1522 * under the control of a mask.
1523 *
1524 * This is a masked lane-wise binary operation which applies
1525 * the primitive addition operation ({@code +})
1526 * to each pair of corresponding lane values.
1527 *
1528 * For any lane unset in the mask, the primitive operation is
1529 * suppressed and this vector retains the original value stored in
1530 * that lane.
1531 *
1532 * This method is also equivalent to the expression
1533 * {@link #lanewise(VectorOperators.Binary,Vector,VectorMask)
1534 * lanewise}{@code (}{@link VectorOperators#ADD
1535 * ADD}{@code , v, m)}.
1536 *
1537 * <p>
1538 * As a full-service named operation, this method
1539 * comes in masked and unmasked overloadings, and
1540 * (in subclasses) also comes in scalar-broadcast
1541 * overloadings (both masked and unmasked).
1542 *
1543 * @param v the second input vector
1544 * @param m the mask controlling lane selection
1545 * @return the result of adding this vector to the given vector
1546 * @see #add(Vector)
1547 * @see IntVector#add(int,VectorMask)
1548 * @see VectorOperators#ADD
1549 * @see #lanewise(VectorOperators.Binary,Vector,VectorMask)
1550 * @see IntVector#lanewise(VectorOperators.Binary,int,VectorMask)
1551 */
1552 public abstract Vector<E> add(Vector<E> v, VectorMask<E> m);
1553
1554 /**
1555 * Subtracts a second input vector from this vector.
1556 *
1557 * This is a lane-wise binary operation which applies
1558 * the primitive subtraction operation ({@code -})
1559 * to each pair of corresponding lane values.
1560 *
1561 * This method is also equivalent to the expression
1562 * {@link #lanewise(VectorOperators.Binary,Vector)
1563 * lanewise}{@code (}{@link VectorOperators#SUB
1564 * SUB}{@code , v)}.
1565 *
1566 * <p>
1567 * As a full-service named operation, this method
1568 * comes in masked and unmasked overloadings, and
1569 * (in subclasses) also comes in scalar-broadcast
1570 * overloadings (both masked and unmasked).
1571 *
1572 * @param v a second input vector
1573 * @return the result of subtracting the second input vector from this vector
1574 * @see #sub(Vector,VectorMask)
1575 * @see IntVector#sub(int)
1576 * @see VectorOperators#SUB
1577 * @see #lanewise(VectorOperators.Binary,Vector)
1578 * @see IntVector#lanewise(VectorOperators.Binary,int)
1579 */
1580 public abstract Vector<E> sub(Vector<E> v);
1581
1582 /**
1583 * Subtracts a second input vector from this vector
1584 * under the control of a mask.
1585 *
1586 * This is a masked lane-wise binary operation which applies
1587 * the primitive subtraction operation ({@code -})
1588 * to each pair of corresponding lane values.
1589 *
1590 * For any lane unset in the mask, the primitive operation is
1591 * suppressed and this vector retains the original value stored in
1592 * that lane.
1593 *
1594 * This method is also equivalent to the expression
1595 * {@link #lanewise(VectorOperators.Binary,Vector,VectorMask)
1596 * lanewise}{@code (}{@link VectorOperators#SUB
1597 * SUB}{@code , v, m)}.
1598 *
1599 * <p>
1600 * As a full-service named operation, this method
1601 * comes in masked and unmasked overloadings, and
1602 * (in subclasses) also comes in scalar-broadcast
1603 * overloadings (both masked and unmasked).
1604 *
1605 * @param v the second input vector
1606 * @param m the mask controlling lane selection
1607 * @return the result of subtracting the second input vector from this vector
1608 * @see #sub(Vector)
1609 * @see IntVector#sub(int,VectorMask)
1610 * @see VectorOperators#SUB
1611 * @see #lanewise(VectorOperators.Binary,Vector,VectorMask)
1612 * @see IntVector#lanewise(VectorOperators.Binary,int,VectorMask)
1613 */
1614 public abstract Vector<E> sub(Vector<E> v, VectorMask<E> m);
1615
1616 /**
1617 * Multiplies this vector by a second input vector.
1618 *
1619 * This is a lane-wise binary operation which applies
1620 * the primitive multiplication operation ({@code *})
1621 * to each pair of corresponding lane values.
1622 *
1623 * This method is also equivalent to the expression
1624 * {@link #lanewise(VectorOperators.Binary,Vector)
1625 * lanewise}{@code (}{@link VectorOperators#MUL
1626 * MUL}{@code , v)}.
1627 *
1628 * <p>
1629 * As a full-service named operation, this method
1630 * comes in masked and unmasked overloadings, and
1631 * (in subclasses) also comes in scalar-broadcast
1632 * overloadings (both masked and unmasked).
1633 *
1634 * @param v a second input vector
1635 * @return the result of multiplying this vector by the second input vector
1636 * @see #mul(Vector,VectorMask)
1637 * @see IntVector#mul(int)
1638 * @see VectorOperators#MUL
1639 * @see #lanewise(VectorOperators.Binary,Vector)
1640 * @see IntVector#lanewise(VectorOperators.Binary,int)
1641 */
1642 public abstract Vector<E> mul(Vector<E> v);
1643
1644 /**
1645 * Multiplies this vector by a second input vector
1646 * under the control of a mask.
1647 *
1648 * This is a lane-wise binary operation which applies
1649 * the primitive multiplication operation ({@code *})
1650 * to each pair of corresponding lane values.
1651 *
1652 * For any lane unset in the mask, the primitive operation is
1653 * suppressed and this vector retains the original value stored in
1654 * that lane.
1655 *
1656 * This method is also equivalent to the expression
1657 * {@link #lanewise(VectorOperators.Binary,Vector,VectorMask)
1658 * lanewise}{@code (}{@link VectorOperators#MUL
1659 * MUL}{@code , v, m)}.
1660 *
1661 * <p>
1662 * As a full-service named operation, this method
1663 * comes in masked and unmasked overloadings, and
1664 * (in subclasses) also comes in scalar-broadcast
1665 * overloadings (both masked and unmasked).
1666 *
1667 * @param v the second input vector
1668 * @param m the mask controlling lane selection
1669 * @return the result of multiplying this vector by the given vector
1670 * @see #mul(Vector)
1671 * @see IntVector#mul(int,VectorMask)
1672 * @see VectorOperators#MUL
1673 * @see #lanewise(VectorOperators.Binary,Vector,VectorMask)
1674 * @see IntVector#lanewise(VectorOperators.Binary,int,VectorMask)
1675 */
1676 public abstract Vector<E> mul(Vector<E> v, VectorMask<E> m);
1677
1678 /**
1679 * Divides this vector by a second input vector.
1680 *
1681 * This is a lane-wise binary operation which applies
1682 * the primitive division operation ({@code /})
1683 * to each pair of corresponding lane values.
1684 *
1685 * This method is also equivalent to the expression
1686 * {@link #lanewise(VectorOperators.Binary,Vector)
1687 * lanewise}{@code (}{@link VectorOperators#DIV
1688 * DIV}{@code , v)}.
1689 *
1690 * <p>
1691 * As a full-service named operation, this method
1692 * comes in masked and unmasked overloadings, and
1693 * (in subclasses) also comes in scalar-broadcast
1694 * overloadings (both masked and unmasked).
1695 *
1696 * @apiNote If the underlying scalar operator does not support
1697 * division by zero, but is presented with a zero divisor,
1698 * an {@code ArithmeticException} will be thrown.
1699 *
1700 * @param v a second input vector
1701 * @return the result of dividing this vector by the second input vector
1702 * @throws ArithmeticException if any lane
1703 * in {@code v} is zero
1704 * and {@code ETYPE} is not {@code float} or {@code double}.
1705 * @see #div(Vector,VectorMask)
1706 * @see DoubleVector#div(double)
1707 * @see VectorOperators#DIV
1708 * @see #lanewise(VectorOperators.Binary,Vector)
1709 * @see IntVector#lanewise(VectorOperators.Binary,int)
1710 */
1711 public abstract Vector<E> div(Vector<E> v);
1712
1713 /**
1714 * Divides this vector by a second input vector
1715 * under the control of a mask.
1716 *
1717 * This is a lane-wise binary operation which applies
1718 * the primitive division operation ({@code /})
1719 * to each pair of corresponding lane values.
1720 *
1721 * For any lane unset in the mask, the primitive operation is
1722 * suppressed and this vector retains the original value stored in
1723 * that lane.
1724 *
1725 * This method is also equivalent to the expression
1726 * {@link #lanewise(VectorOperators.Binary,Vector,VectorMask)
1727 * lanewise}{@code (}{@link VectorOperators#DIV
1728 * DIV}{@code , v, m)}.
1729 *
1730 * <p>
1731 * As a full-service named operation, this method
1732 * comes in masked and unmasked overloadings, and
1733 * (in subclasses) also comes in scalar-broadcast
1734 * overloadings (both masked and unmasked).
1735 *
1736 * @apiNote If the underlying scalar operator does not support
1737 * division by zero, but is presented with a zero divisor,
1738 * an {@code ArithmeticException} will be thrown.
1739 *
1740 * @param v a second input vector
1741 * @param m the mask controlling lane selection
1742 * @return the result of dividing this vector by the second input vector
1743 * @throws ArithmeticException if any lane selected by {@code m}
1744 * in {@code v} is zero
1745 * and {@code ETYPE} is not {@code float} or {@code double}.
1746 * @see #div(Vector)
1747 * @see DoubleVector#div(double,VectorMask)
1748 * @see VectorOperators#DIV
1749 * @see #lanewise(VectorOperators.Binary,Vector,VectorMask)
1750 * @see DoubleVector#lanewise(VectorOperators.Binary,double,VectorMask)
1751 */
1752 public abstract Vector<E> div(Vector<E> v, VectorMask<E> m);
1753
1754 /// END OF FULL-SERVICE BINARY METHODS
1755
1756 /// Non-full-service unary ops: NEG, ABS
1757
1758 /**
1759 * Negates this vector.
1760 *
1761 * This is a lane-wise unary operation which applies
1762 * the primitive negation operation ({@code -x})
1763 * to each input lane.
1764 *
1765 * This method is also equivalent to the expression
1766 * {@link #lanewise(VectorOperators.Unary)
1767 * lanewise}{@code (}{@link VectorOperators#NEG
1768 * NEG}{@code )}.
1769 *
1770 * @apiNote
1771 * This method has no masked variant, but the corresponding
1772 * masked operation can be obtained from the
1773 * {@linkplain #lanewise(VectorOperators.Unary,VectorMask)
1774 * lanewise method}.
1775 *
1776 * @return the negation of this vector
1777 * @see VectorOperators#NEG
1778 * @see #lanewise(VectorOperators.Unary)
1779 * @see #lanewise(VectorOperators.Unary,VectorMask)
1780 */
1781 public abstract Vector<E> neg();
1782
1783 /**
1784 * Returns the absolute value of this vector.
1785 *
1786 * This is a lane-wise unary operation which applies
1787 * the method {@code Math.abs}
1788 * to each input lane.
1789 *
1790 * This method is also equivalent to the expression
1791 * {@link #lanewise(VectorOperators.Unary)
1792 * lanewise}{@code (}{@link VectorOperators#ABS
1793 * ABS}{@code )}.
1794 *
1795 * @apiNote
1796 * This method has no masked variant, but the corresponding
1797 * masked operation can be obtained from the
1798 * {@linkplain #lanewise(VectorOperators.Unary,VectorMask)
1799 * lanewise method}.
1800 *
1801 * @return the absolute value of this vector
1802 * @see VectorOperators#ABS
1803 * @see #lanewise(VectorOperators.Unary)
1804 * @see #lanewise(VectorOperators.Unary,VectorMask)
1805 */
1806 public abstract Vector<E> abs();
1807
1808 /// Non-full-service binary ops: MIN, MAX
1809
1810 /**
1811 * Computes the smaller of this vector and a second input vector.
1812 *
1813 * This is a lane-wise binary operation which applies the
1814 * operation {@code Math.min()} to each pair of
1815 * corresponding lane values.
1816 *
1817 * This method is also equivalent to the expression
1818 * {@link #lanewise(VectorOperators.Binary,Vector)
1819 * lanewise}{@code (}{@link VectorOperators#MIN
1820 * MIN}{@code , v)}.
1821 *
1822 * @apiNote
1823 * This is not a full-service named operation like
1824 * {@link #add(Vector) add()}. A masked version of
1825 * this operation is not directly available
1826 * but may be obtained via the masked version of
1827 * {@code lanewise}. Subclasses define an additional
1828 * scalar-broadcast overloading of this method.
1829 *
1830 * @param v a second input vector
1831 * @return the lanewise minimum of this vector and the second input vector
1832 * @see IntVector#min(int)
1833 * @see VectorOperators#MIN
1834 * @see #lanewise(VectorOperators.Binary,Vector)
1835 * @see #lanewise(VectorOperators.Binary,Vector,VectorMask)
1836 */
1837 public abstract Vector<E> min(Vector<E> v);
1838
1839 /**
1840 * Computes the larger of this vector and a second input vector.
1841 *
1842 * This is a lane-wise binary operation which applies the
1843 * operation {@code Math.max()} to each pair of
1844 * corresponding lane values.
1845 *
1846 * This method is also equivalent to the expression
1847 * {@link #lanewise(VectorOperators.Binary,Vector)
1848 * lanewise}{@code (}{@link VectorOperators#MAX
1849 * MAX}{@code , v)}.
1850 *
1851 * <p>
1852 * This is not a full-service named operation like
1853 * {@link #add(Vector) add()}. A masked version of
1854 * this operation is not directly available
1855 * but may be obtained via the masked version of
1856 * {@code lanewise}. Subclasses define an additional
1857 * scalar-broadcast overloading of this method.
1858 *
1859 * @param v a second input vector
1860 * @return the lanewise maximum of this vector and the second input vector
1861 * @see IntVector#max(int)
1862 * @see VectorOperators#MAX
1863 * @see #lanewise(VectorOperators.Binary,Vector)
1864 * @see #lanewise(VectorOperators.Binary,Vector,VectorMask)
1865 */
1866 public abstract Vector<E> max(Vector<E> v);
1867
1868 // Reductions
1869
1870 /**
1871 * Returns a value accumulated from all the lanes of this vector.
1872 *
1873 * This is an associative cross-lane reduction operation which
1874 * applies the specified operation to all the lane elements.
1875 * The return value will be equal to this expression:
1876 * {@code (long) ((EVector)this).reduceLanes(op)}, where {@code EVector}
1877 * is the vector class specific to this vector's element type
1878 * {@code ETYPE}.
1879 * <p>
1880 * In the case of operations {@code ADD} and {@code MUL},
1881 * when {@code ETYPE} is {@code float} or {@code double},
1882 * the precise result, before casting, will reflect the choice
1883 * of an arbitrary order of operations, which may even vary over time.
1884 * For further details see the section
1885 * <a href="VectorOperators.html#fp_assoc">Operations on floating point vectors</a>.
1886 *
1887 * @apiNote
1888 * If the {@code ETYPE} is {@code float} or {@code double},
1889 * this operation can lose precision and/or range, as a
1890 * normal part of casting the result down to {@code long}.
1891 *
1892 * Usually
1893 * {@linkplain IntVector#reduceLanes(VectorOperators.Associative)
1894 * strongly typed access}
1895 * is preferable, if you are working with a vector
1896 * subtype that has a known element type.
1897 *
1898 * @param op the operation used to combine lane values
1899 * @return the accumulated result, cast to {@code long}
1900 * @throws UnsupportedOperationException if this vector does
1901 * not support the requested operation
1902 * @see #reduceLanesToLong(VectorOperators.Associative,VectorMask)
1903 * @see IntVector#reduceLanes(VectorOperators.Associative)
1904 * @see FloatVector#reduceLanes(VectorOperators.Associative)
1905 */
1906 public abstract long reduceLanesToLong(VectorOperators.Associative op);
1907
1908 /**
1909 * Returns a value accumulated from selected lanes of this vector,
1910 * controlled by a mask.
1911 *
1912 * This is an associative cross-lane reduction operation which
1913 * applies the specified operation to the selected lane elements.
1914 * The return value will be equal to this expression:
1915 * {@code (long) ((EVector)this).reduceLanes(op, m)}, where {@code EVector}
1916 * is the vector class specific to this vector's element type
1917 * {@code ETYPE}.
1918 * <p>
1919 * If no elements are selected, an operation-specific identity
1920 * value is returned.
1921 * <ul>
1922 * <li>
1923 * If the operation is {@code ADD}, {@code XOR}, or {@code OR},
1924 * then the identity value is zero.
1925 * <li>
1926 * If the operation is {@code MUL},
1927 * then the identity value is one.
1928 * <li>
1929 * If the operation is {@code AND},
1930 * then the identity value is minus one (all bits set).
1931 * <li>
1932 * If the operation is {@code MAX},
1933 * then the identity value is the {@code MIN_VALUE}
1934 * of the vector's native {@code ETYPE}.
1935 * (In the case of floating point types, the value
1936 * {@code NEGATIVE_INFINITY} is used, and will appear
1937 * after casting as {@code Long.MIN_VALUE}.
1938 * <li>
1939 * If the operation is {@code MIN},
1940 * then the identity value is the {@code MAX_VALUE}
1941 * of the vector's native {@code ETYPE}.
1942 * (In the case of floating point types, the value
1943 * {@code POSITIVE_INFINITY} is used, and will appear
1944 * after casting as {@code Long.MAX_VALUE}.
1945 * </ul>
1946 * <p>
1947 * In the case of operations {@code ADD} and {@code MUL},
1948 * when {@code ETYPE} is {@code float} or {@code double},
1949 * the precise result, before casting, will reflect the choice
1950 * of an arbitrary order of operations, which may even vary over time.
1951 * For further details see the section
1952 * <a href="VectorOperators.html#fp_assoc">Operations on floating point vectors</a>.
1953 *
1954 * @apiNote
1955 * If the {@code ETYPE} is {@code float} or {@code double},
1956 * this operation can lose precision and/or range, as a
1957 * normal part of casting the result down to {@code long}.
1958 *
1959 * Usually
1960 * {@linkplain IntVector#reduceLanes(VectorOperators.Associative,VectorMask)
1961 * strongly typed access}
1962 * is preferable, if you are working with a vector
1963 * subtype that has a known element type.
1964 *
1965 * @param op the operation used to combine lane values
1966 * @param m the mask controlling lane selection
1967 * @return the reduced result accumulated from the selected lane values
1968 * @throws UnsupportedOperationException if this vector does
1969 * not support the requested operation
1970 * @see #reduceLanesToLong(VectorOperators.Associative)
1971 * @see IntVector#reduceLanes(VectorOperators.Associative,VectorMask)
1972 * @see FloatVector#reduceLanes(VectorOperators.Associative,VectorMask)
1973 */
1974 public abstract long reduceLanesToLong(VectorOperators.Associative op,
1975 VectorMask<E> m);
1976
1977 // Lanewise unary tests
1978
1979 /**
1980 * Tests the lanes of this vector
1981 * according to the given operation.
1982 *
1983 * This is a lane-wise unary test operation which applies
1984 * the given test operation
1985 * to each lane value.
1986 * @param op the operation used to test lane values
1987 * @return the mask result of testing the lanes of this vector,
1988 * according to the selected test operator
1989 * @see VectorOperators.Comparison
1990 * @see #test(VectorOperators.Test, VectorMask)
1991 * @see #compare(VectorOperators.Comparison, Vector)
1992 */
1993 public abstract VectorMask<E> test(VectorOperators.Test op);
1994
1995 /**
1996 * Test selected lanes of this vector,
1997 * according to the given operation.
1998 *
1999 * This is a masked lane-wise unary test operation which applies
2000 * the given test operation
2001 * to each lane value.
2002 *
2003 * The returned result is equal to the expression
2004 * {@code test(op).and(m)}.
2005 *
2006 * @param op the operation used to test lane values
2007 * @param m the mask controlling lane selection
2008 * @return the mask result of testing the lanes of this vector,
2009 * according to the selected test operator,
2010 * and only in the lanes selected by the mask
2011 * @see #test(VectorOperators.Test)
2012 */
2013 public abstract VectorMask<E> test(VectorOperators.Test op,
2014 VectorMask<E> m);
2015
2016 // Comparisons
2017
2018 /**
2019 * Tests if this vector is equal to another input vector.
2020 *
2021 * This is a lane-wise binary test operation which applies
2022 * the primitive equals operation ({@code ==})
2023 * to each pair of corresponding lane values.
2024 * The result is the same as {@code compare(VectorOperators.EQ, v)}.
2025 *
2026 * @param v a second input vector
2027 * @return the mask result of testing lane-wise if this vector
2028 * equal to the second input vector
2029 * @see #compare(VectorOperators.Comparison,Vector)
2030 * @see VectorOperators#EQ
2031 * @see #equals
2032 */
2033 public abstract VectorMask<E> eq(Vector<E> v);
2034
2035 /**
2036 * Tests if this vector is less than another input vector.
2037 *
2038 * This is a lane-wise binary test operation which applies
2039 * the primitive less-than operation ({@code <}) to each lane.
2040 * The result is the same as {@code compare(VectorOperators.LT, v)}.
2041 *
2042 * @param v a second input vector
2043 * @return the mask result of testing lane-wise if this vector
2044 * is less than the second input vector
2045 * @see #compare(VectorOperators.Comparison,Vector)
2046 * @see VectorOperators#LT
2047 */
2048 public abstract VectorMask<E> lt(Vector<E> v);
2049
2050 /**
2051 * Tests this vector by comparing it with another input vector,
2052 * according to the given comparison operation.
2053 *
2054 * This is a lane-wise binary test operation which applies
2055 * the given comparison operation
2056 * to each pair of corresponding lane values.
2057 *
2058 * @param op the operation used to compare lane values
2059 * @param v a second input vector
2060 * @return the mask result of testing lane-wise if this vector
2061 * compares to the input, according to the selected
2062 * comparison operator
2063 * @see #eq(Vector)
2064 * @see #lt(Vector)
2065 * @see VectorOperators.Comparison
2066 * @see #compare(VectorOperators.Comparison, Vector, VectorMask)
2067 * @see #test(VectorOperators.Test)
2068 */
2069 public abstract VectorMask<E> compare(VectorOperators.Comparison op,
2070 Vector<E> v);
2071
2072 /**
2073 * Tests this vector by comparing it with another input vector,
2074 * according to the given comparison operation,
2075 * in lanes selected by a mask.
2076 *
2077 * This is a masked lane-wise binary test operation which applies
2078 * the given comparison operation
2079 * to each pair of corresponding lane values.
2080 *
2081 * The returned result is equal to the expression
2082 * {@code compare(op,v).and(m)}.
2083 *
2084 * @param op the operation used to compare lane values
2085 * @param v a second input vector
2086 * @param m the mask controlling lane selection
2087 * @return the mask result of testing lane-wise if this vector
2088 * compares to the input, according to the selected
2089 * comparison operator,
2090 * and only in the lanes selected by the mask
2091 * @see #compare(VectorOperators.Comparison, Vector)
2092 */
2093 public abstract VectorMask<E> compare(VectorOperators.Comparison op,
2094 Vector<E> v,
2095 VectorMask<E> m);
2096
2097 /**
2098 * Tests this vector by comparing it with an input scalar,
2099 * according to the given comparison operation.
2100 *
2101 * This is a lane-wise binary test operation which applies
2102 * the given comparison operation
2103 * to each lane value, paired with the broadcast value.
2104 *
2105 * <p>
2106 * The result is the same as
2107 * {@code this.compare(op, this.broadcast(e))}.
2108 * That is, the scalar may be regarded as broadcast to
2109 * a vector of the same species, and then compared
2110 * against the original vector, using the selected
2111 * comparison operation.
2112 *
2113 * @apiNote
2114 * The {@code long} value {@code e} must be accurately
2115 * representable by the {@code ETYPE} of this vector's species,
2116 * so that {@code e==(long)(ETYPE)e}. This rule is enforced
2117 * by the implicit call to {@code broadcast()}.
2118 * <p>
2119 * Subtypes improve on this method by sharpening
2120 * the type of the scalar parameter {@code e}.
2121 *
2122 * @param op the operation used to compare lane values
2123 * @param e the input scalar
2124 * @return the mask result of testing lane-wise if this vector
2125 * compares to the input, according to the selected
2126 * comparison operator
2127 * @throws IllegalArgumentException
2128 * if the given {@code long} value cannot
2129 * be represented by the vector's {@code ETYPE}
2130 * @see #broadcast(long)
2131 * @see #compare(VectorOperators.Comparison,Vector)
2132 */
2133 public abstract VectorMask<E> compare(VectorOperators.Comparison op,
2134 long e);
2135
2136 /**
2137 * Tests this vector by comparing it with an input scalar,
2138 * according to the given comparison operation,
2139 * in lanes selected by a mask.
2140 *
2141 * This is a masked lane-wise binary test operation which applies
2142 * the given comparison operation
2143 * to each lane value, paired with the broadcast value.
2144 *
2145 * The returned result is equal to the expression
2146 * {@code compare(op,e).and(m)}.
2147 *
2148 * @apiNote
2149 * The {@code long} value {@code e} must be accurately
2150 * representable by the {@code ETYPE} of this vector's species,
2151 * so that {@code e==(long)(ETYPE)e}. This rule is enforced
2152 * by the implicit call to {@code broadcast()}.
2153 * <p>
2154 * Subtypes improve on this method by sharpening
2155 * the type of the scalar parameter {@code e}.
2156 *
2157 * @param op the operation used to compare lane values
2158 * @param e the input scalar
2159 * @param m the mask controlling lane selection
2160 * @return the mask result of testing lane-wise if this vector
2161 * compares to the input, according to the selected
2162 * comparison operator,
2163 * and only in the lanes selected by the mask
2164 * @throws IllegalArgumentException
2165 * if the given {@code long} value cannot
2166 * be represented by the vector's {@code ETYPE}
2167 * @see #broadcast(long)
2168 * @see #compare(VectorOperators.Comparison,Vector)
2169 */
2170 public abstract VectorMask<E> compare(VectorOperators.Comparison op,
2171 long e,
2172 VectorMask<E> m);
2173
2174 /**
2175 * Replaces selected lanes of this vector with
2176 * corresponding lanes from a second input vector
2177 * under the control of a mask.
2178 *
2179 * This is a masked lane-wise binary operation which
2180 * selects each lane value from one or the other input.
2181 *
2182 * <ul>
2183 * <li>
2184 * For any lane <em>set</em> in the mask, the new lane value
2185 * is taken from the second input vector, and replaces
2186 * whatever value was in the that lane of this vector.
2187 * <li>
2188 * For any lane <em>unset</em> in the mask, the replacement is
2189 * suppressed and this vector retains the original value stored in
2190 * that lane.
2191 * </ul>
2192 *
2193 * The following pseudocode illustrates this behavior:
2194 * <pre>{@code
2195 * Vector<E> a = ...;
2196 * VectorSpecies<E> species = a.species();
2197 * Vector<E> b = ...;
2198 * b.check(species);
2199 * VectorMask<E> m = ...;
2200 * ETYPE[] ar = a.toArray();
2201 * for (int i = 0; i < ar.length; i++) {
2202 * if (m.laneIsSet(i)) {
2203 * ar[i] = b.lane(i);
2204 * }
2205 * }
2206 * return EVector.fromArray(s, ar, 0);
2207 * }</pre>
2208 *
2209 * @param v the second input vector, containing replacement lane values
2210 * @param m the mask controlling lane selection from the second input vector
2211 * @return the result of blending the lane elements of this vector with
2212 * those of the second input vector
2213 */
2214 public abstract Vector<E> blend(Vector<E> v, VectorMask<E> m);
2215
2216 /**
2217 * Replaces selected lanes of this vector with
2218 * a scalar value
2219 * under the control of a mask.
2220 *
2221 * This is a masked lane-wise binary operation which
2222 * selects each lane value from one or the other input.
2223 *
2224 * The returned result is equal to the expression
2225 * {@code blend(broadcast(e),m)}.
2226 *
2227 * @apiNote
2228 * The {@code long} value {@code e} must be accurately
2229 * representable by the {@code ETYPE} of this vector's species,
2230 * so that {@code e==(long)(ETYPE)e}. This rule is enforced
2231 * by the implicit call to {@code broadcast()}.
2232 * <p>
2233 * Subtypes improve on this method by sharpening
2234 * the type of the scalar parameter {@code e}.
2235 *
2236 * @param e the input scalar, containing the replacement lane value
2237 * @param m the mask controlling lane selection of the scalar
2238 * @return the result of blending the lane elements of this vector with
2239 * the scalar value
2240 */
2241 public abstract Vector<E> blend(long e, VectorMask<E> m);
2242
2243 /**
2244 * Adds the lanes of this vector to their corresponding
2245 * lane numbers, scaled by a given constant.
2246 *
2247 * This is a lane-wise unary operation which, for
2248 * each lane {@code N}, computes the scaled index value
2249 * {@code N*scale} and adds it to the value already
2250 * in lane {@code N} of the current vector.
2251 *
2252 * <p> The scale must not be so large, and the element size must
2253 * not be so small, that that there would be an overflow when
2254 * computing any of the {@code N*scale} or {@code VLENGTH*scale},
2255 * when the the result is represented using the vector
2256 * lane type {@code ETYPE}.
2257 *
2258 * <p>
2259 * The following pseudocode illustrates this behavior:
2260 * <pre>{@code
2261 * Vector<E> a = ...;
2262 * VectorSpecies<E> species = a.species();
2263 * ETYPE[] ar = a.toArray();
2264 * for (int i = 0; i < ar.length; i++) {
2265 * long d = (long)i * scale;
2266 * if (d != (ETYPE) d) throw ...;
2267 * ar[i] += (ETYPE) d;
2268 * }
2269 * long d = (long)ar.length * scale;
2270 * if (d != (ETYPE) d) throw ...;
2271 * return EVector.fromArray(s, ar, 0);
2272 * }</pre>
2273 *
2274 * @param scale the number to multiply by each lane index
2275 * {@code N}, typically {@code 1}
2276 * @return the result of incrementing each lane element by its
2277 * corresponding lane index {@code N}, scaled by {@code scale}
2278 * @throws IllegalArgumentException
2279 * if the values in the interval
2280 * {@code [0..VLENGTH*scale]}
2281 * are not representable by the {@code ETYPE}
2282 */
2283 public abstract Vector<E> addIndex(int scale);
2284
2285 // Slicing segments of adjacent lanes
2286
2287 /**
2288 * Slices a segment of adjacent lanes, starting at a given
2289 * {@code origin} lane in the current vector, and continuing (as
2290 * needed) into an immediately following vector. The block of
2291 * {@code VLENGTH} lanes is extracted into its own vector and
2292 * returned.
2293 *
2294 * <p> This is a cross-lane operation that shifts lane elements
2295 * to the front, from the current vector and the second vector.
2296 * Both vectors can be viewed as a combined "background" of length
2297 * {@code 2*VLENGTH}, from which a slice is extracted.
2298 *
2299 * The lane numbered {@code N} in the output vector is copied
2300 * from lane {@code origin+N} of the input vector, if that
2301 * lane exists, else from lane {@code origin+N-VLENGTH} of
2302 * the second vector (which is guaranteed to exist).
2303 *
2304 * <p> The {@code origin} value must be in the inclusive range
2305 * {@code 0..VLENGTH}. As limiting cases, {@code v.slice(0,w)}
2306 * and {@code v.slice(VLENGTH,w)} return {@code v} and {@code w},
2307 * respectively.
2308 *
2309 * @apiNote
2310 *
2311 * This method may be regarded as the inverse of
2312 * {@link #unslice(int,Vector,int) unslice()},
2313 * in that the sliced value could be unsliced back into its
2314 * original position in the two input vectors, without
2315 * disturbing unrelated elements, as in the following
2316 * pseudocode:
2317 * <pre>{@code
2318 * EVector slice = v1.slice(origin, v2);
2319 * EVector w1 = slice.unslice(origin, v1, 0);
2320 * EVector w2 = slice.unslice(origin, v2, 1);
2321 * assert v1.equals(w1);
2322 * assert v2.equals(w2);
2323 * }</pre>
2324 *
2325 * <p> This method also supports a variety of cross-lane shifts and
2326 * rotates as follows:
2327 * <ul>
2328 *
2329 * <li>To shift lanes forward to the front of the vector, supply a
2330 * zero vector for the second operand and specify the shift count
2331 * as the origin. For example: {@code v.slice(shift, v.broadcast(0))}.
2332 *
2333 * <li>To shift lanes backward to the back of the vector, supply a
2334 * zero vector for the <em>first</em> operand, and specify the
2335 * negative shift count as the origin (modulo {@code VLENGTH}.
2336 * For example: {@code v.broadcast(0).slice(v.length()-shift, v)}.
2337 *
2338 * <li>To rotate lanes forward toward the front end of the vector,
2339 * cycling the earliest lanes around to the back, supply the same
2340 * vector for both operands and specify the rotate count as the
2341 * origin. For example: {@code v.slice(rotate, v)}.
2342 *
2343 * <li>To rotate lanes backward toward the back end of the vector,
2344 * cycling the latest lanes around to the front, supply the same
2345 * vector for both operands and specify the negative of the rotate
2346 * count (modulo {@code VLENGTH}) as the origin. For example:
2347 * {@code v.slice(v.length() - rotate, v)}.
2348 *
2349 * <li>
2350 * Since {@code origin} values less then zero or more than
2351 * {@code VLENGTH} will be rejected, if you need to rotate
2352 * by an unpredictable multiple of {@code VLENGTH}, be sure
2353 * to reduce the origin value into the required range.
2354 * The {@link VectorSpecies#loopBound(int) loopBound()}
2355 * method can help with this. For example:
2356 * {@code v.slice(rotate - v.species().loopBound(rotate), v)}.
2357 *
2358 * </ul>
2359 *
2360 * @param origin the first input lane to transfer into the slice
2361 * @param v1 a second vector logically concatenated with the first,
2362 * before the slice is taken (if omitted it defaults to zero)
2363 * @return a contiguous slice of {@code VLENGTH} lanes, taken from
2364 * this vector starting at the indicated origin, and
2365 * continuing (as needed) into the second vector
2366 * @throws ArrayIndexOutOfBoundsException if {@code origin}
2367 * is negative or greater than {@code VLENGTH}
2368 * @see #slice(int,Vector,VectorMask)
2369 * @see #slice(int)
2370 * @see #unslice(int,Vector,int)
2371 */
2372 public abstract Vector<E> slice(int origin, Vector<E> v1);
2373
2374 /**
2375 * Slices a segment of adjacent lanes
2376 * under the control of a mask,
2377 * starting at a given
2378 * {@code origin} lane in the current vector, and continuing (as
2379 * needed) into an immediately following vector. The block of
2380 * {@code VLENGTH} lanes is extracted into its own vector and
2381 * returned.
2382 *
2383 * The resulting vector will be zero in all lanes unset in the
2384 * given mask. Lanes set in the mask will contain data copied
2385 * from selected lanes of {@code this} or {@code v1}.
2386 *
2387 * <p> This is a cross-lane operation that shifts lane elements
2388 * to the front, from the current vector and the second vector.
2389 * Both vectors can be viewed as a combined "background" of length
2390 * {@code 2*VLENGTH}, from which a slice is extracted.
2391 *
2392 * The returned result is equal to the expression
2393 * {@code broadcast(0).blend(slice(origin,v1),m)}.
2394 *
2395 * @apiNote
2396 * This method may be regarded as the inverse of
2397 * {@code #unslice(int,Vector,int,VectorMask) unslice()},
2398 * in that the sliced value could be unsliced back into its
2399 * original position in the two input vectors, without
2400 * disturbing unrelated elements, as in the following
2401 * pseudocode:
2402 * <pre>{@code
2403 * EVector slice = v1.slice(origin, v2, m);
2404 * EVector w1 = slice.unslice(origin, v1, 0, m);
2405 * EVector w2 = slice.unslice(origin, v2, 1, m);
2406 * assert v1.equals(w1);
2407 * assert v2.equals(w2);
2408 * }</pre>
2409 *
2410 * @param origin the first input lane to transfer into the slice
2411 * @param v1 a second vector logically concatenated with the first,
2412 * before the slice is taken (if omitted it defaults to zero)
2413 * @param m the mask controlling lane selection into the resulting vector
2414 * @return a contiguous slice of {@code VLENGTH} lanes, taken from
2415 * this vector starting at the indicated origin, and
2416 * continuing (as needed) into the second vector
2417 * @throws ArrayIndexOutOfBoundsException if {@code origin}
2418 * is negative or greater than {@code VLENGTH}
2419 * @see #slice(int,Vector)
2420 * @see #unslice(int,Vector,int,VectorMask)
2421 */
2422 // This doesn't pull its weight, but its symmetrical with
2423 // masked unslice, and might cause questions if missing.
2424 // It could make for clearer code.
2425 public abstract Vector<E> slice(int origin, Vector<E> v1, VectorMask<E> m);
2426
2427 /**
2428 * Slices a segment of adjacent lanes, starting at a given
2429 * {@code origin} lane in the current vector. A block of
2430 * {@code VLENGTH} lanes, possibly padded with zero lanes, is
2431 * extracted into its own vector and returned.
2432 *
2433 * This is a convenience method which slices from a single
2434 * vector against an extended background of zero lanes.
2435 * It is equivalent to
2436 * {@link #slice(int,Vector) slice}{@code
2437 * (origin, }{@link #broadcast(long) broadcast}{@code (0))}.
2438 * It may also be viewed simply as a cross-lane shift
2439 * from later to earlier lanes, with zeroes filling
2440 * in the vacated lanes at the end of the vector.
2441 * In this view, the shift count is {@code origin}.
2442 *
2443 * @param origin the first input lane to transfer into the slice
2444 * @return the last {@code VLENGTH-origin} input lanes,
2445 * placed starting in the first lane of the ouput,
2446 * padded at the end with zeroes
2447 * @throws ArrayIndexOutOfBoundsException if {@code origin}
2448 * is negative or greater than {@code VLENGTH}
2449 * @see #slice(int,Vector)
2450 * @see #unslice(int,Vector,int)
2451 */
2452 // This API point pulls its weight as a teaching aid,
2453 // though it's a one-off and broadcast(0) is easy.
2454 public abstract Vector<E> slice(int origin);
2455
2456 /**
2457 * Reverses a {@linkplain #slice(int,Vector) slice()}, inserting
2458 * the current vector as a slice within another "background" input
2459 * vector, which is regarded as one or the other input to a
2460 * hypothetical subsequent {@code slice()} operation.
2461 *
2462 * <p> This is a cross-lane operation that permutes the lane
2463 * elements of the current vector toward the back and inserts them
2464 * into a logical pair of background vectors. Only one of the
2465 * pair will be returned, however. The background is formed by
2466 * duplicating the second input vector. (However, the output will
2467 * never contain two duplicates from the same input lane.)
2468 *
2469 * The lane numbered {@code N} in the input vector is copied into
2470 * lane {@code origin+N} of the first background vector, if that
2471 * lane exists, else into lane {@code origin+N-VLENGTH} of the
2472 * second background vector (which is guaranteed to exist).
2473 *
2474 * The first or second background vector, updated with the
2475 * inserted slice, is returned. The {@code part} number of zero
2476 * or one selects the first or second updated background vector.
2477 *
2478 * <p> The {@code origin} value must be in the inclusive range
2479 * {@code 0..VLENGTH}. As limiting cases, {@code v.unslice(0,w,0)}
2480 * and {@code v.unslice(VLENGTH,w,1)} both return {@code v}, while
2481 * {@code v.unslice(0,w,1)} and {@code v.unslice(VLENGTH,w,0)}
2482 * both return {@code w}.
2483 *
2484 * @apiNote
2485 * This method supports a variety of cross-lane insertion
2486 * operations as follows:
2487 * <ul>
2488 *
2489 * <li>To insert near the end of a background vector {@code w}
2490 * at some offset, specify the offset as the origin and
2491 * select part zero. For example: {@code v.unslice(offset, w, 0)}.
2492 *
2493 * <li>To insert near the end of a background vector {@code w},
2494 * but capturing the overflow into the next vector {@code x},
2495 * specify the offset as the origin and select part one.
2496 * For example: {@code v.unslice(offset, x, 1)}.
2497 *
2498 * <li>To insert the last {@code N} items near the beginning
2499 * of a background vector {@code w}, supply a {@code VLENGTH-N}
2500 * as the origin and select part one.
2501 * For example: {@code v.unslice(v.length()-N, w)}.
2502 *
2503 * </ul>
2504 *
2505 * @param origin the first output lane to receive the slice
2506 * @param w the background vector that (as two copies) will receive
2507 * the inserted slice
2508 * @param part the part number of the result (either zero or one)
2509 * @return either the first or second part of a pair of
2510 * background vectors {@code w}, updated by inserting
2511 * this vector at the indicated origin
2512 * @throws ArrayIndexOutOfBoundsException if {@code origin}
2513 * is negative or greater than {@code VLENGTH},
2514 * or if {@code part} is not zero or one
2515 * @see #slice(int,Vector)
2516 * @see #unslice(int,Vector,int,VectorMask)
2517 */
2518 public abstract Vector<E> unslice(int origin, Vector<E> w, int part);
2519
2520 /**
2521 * Reverses a {@linkplain #slice(int,Vector) slice()}, inserting
2522 * (under the control of a mask)
2523 * the current vector as a slice within another "background" input
2524 * vector, which is regarded as one or the other input to a
2525 * hypothetical subsequent {@code slice()} operation.
2526 *
2527 * <p> This is a cross-lane operation that permutes the lane
2528 * elements of the current vector forward and inserts its lanes
2529 * (when selected by the mask) into a logical pair of background
2530 * vectors. As with the
2531 * {@linkplain #unslice(int,Vector,int) unmasked version} of this method,
2532 * only one of the pair will be returned, as selected by the
2533 * {@code part} number.
2534 *
2535 * For each lane {@code N} selected by the mask, the lane value
2536 * is copied into
2537 * lane {@code origin+N} of the first background vector, if that
2538 * lane exists, else into lane {@code origin+N-VLENGTH} of the
2539 * second background vector (which is guaranteed to exist).
2540 * Background lanes retain their original values if the
2541 * corresponding input lanes {@code N} are unset in the mask.
2542 *
2543 * The first or second background vector, updated with set lanes
2544 * of the inserted slice, is returned. The {@code part} number of
2545 * zero or one selects the first or second updated background
2546 * vector.
2547 *
2548 * @param origin the first output lane to receive the slice
2549 * @param w the background vector that (as two copies) will receive
2550 * the inserted slice, if they are set in {@code m}
2551 * @param part the part number of the result (either zero or one)
2552 * @param m the mask controlling lane selection from the current vector
2553 * @return either the first or second part of a pair of
2554 * background vectors {@code w}, updated by inserting
2555 * selected lanes of this vector at the indicated origin
2556 * @throws ArrayIndexOutOfBoundsException if {@code origin}
2557 * is negative or greater than {@code VLENGTH},
2558 * or if {@code part} is not zero or one
2559 * @see #unslice(int,Vector,int)
2560 * @see #slice(int,Vector)
2561 */
2562 public abstract Vector<E> unslice(int origin, Vector<E> w, int part, VectorMask<E> m);
2563
2564 /**
2565 * Reverses a {@linkplain #slice(int) slice()}, inserting
2566 * the current vector as a slice within a "background" input
2567 * of zero lane values. Compared to other {@code unslice()}
2568 * methods, this method only returns the first of the
2569 * pair of background vectors.
2570 *
2571 * This is a convenience method which returns the result of
2572 * {@link #unslice(int,Vector,int) unslice}{@code
2573 * (origin, }{@link #broadcast(long) broadcast}{@code (0), 0)}.
2574 * It may also be viewed simply as a cross-lane shift
2575 * from earlier to later lanes, with zeroes filling
2576 * in the vacated lanes at the beginning of the vector.
2577 * In this view, the shift count is {@code origin}.
2578 *
2579 * @param origin the first output lane to receive the slice
2580 * @return the first {@code VLENGTH-origin} input lanes,
2581 * placed starting at the given origin,
2582 * padded at the beginning with zeroes
2583 * @throws ArrayIndexOutOfBoundsException if {@code origin}
2584 * is negative or greater than {@code VLENGTH}
2585 * @see #unslice(int,Vector,int)
2586 * @see #slice(int)
2587 */
2588 // This API point pulls its weight as a teaching aid,
2589 // though it's a one-off and broadcast(0) is easy.
2590 public abstract Vector<E> unslice(int origin);
2591
2592 // ISSUE: Add a slice which uses a mask instead of an origin?
2593 //public abstract Vector<E> slice(VectorMask<E> support);
2594
2595 // ISSUE: Add some more options for questionable edge conditions?
2596 // We might define enum EdgeOption { ERROR, ZERO, WRAP } for the
2597 // default of throwing AIOOBE, or substituting zeroes, or just
2598 // reducing the out-of-bounds index modulo VLENGTH. Similar
2599 // concerns also apply to general Shuffle operations. For now,
2600 // just support ERROR, since that is safest.
2601
2602 /**
2603 * Rearranges the lane elements of this vector, selecting lanes
2604 * under the control of a specific shuffle.
2605 *
2606 * This is a cross-lane operation that rearranges the lane
2607 * elements of this vector.
2608 *
2609 * For each lane {@code N} of the shuffle, and for each lane
2610 * source index {@code I=s.laneSource(N)} in the shuffle,
2611 * the output lane {@code N} obtains the value from
2612 * the input vector at lane {@code I}.
2613 *
2614 * @param s the shuffle controlling lane index selection
2615 * @return the rearrangement of the lane elements of this vector
2616 * @throws IndexOutOfBoundsException if there are any exceptional
2617 * source indexes in the shuffle
2618 * @see #rearrange(VectorShuffle,VectorMask)
2619 * @see #rearrange(VectorShuffle,Vector)
2620 * @see VectorShuffle#laneIsValid()
2621 */
2622 public abstract Vector<E> rearrange(VectorShuffle<E> s);
2623
2624 /**
2625 * Rearranges the lane elements of this vector, selecting lanes
2626 * under the control of a specific shuffle and a mask.
2627 *
2628 * This is a cross-lane operation that rearranges the lane
2629 * elements of this vector.
2630 *
2631 * For each lane {@code N} of the shuffle, and for each lane
2632 * source index {@code I=s.laneSource(N)} in the shuffle,
2633 * the output lane {@code N} obtains the value from
2634 * the input vector at lane {@code I} if the mask is set.
2635 * Otherwise the output lane {@code N} is set to zero.
2636 *
2637 * <p> This method returns the value of this pseudocode:
2638 * <pre>{@code
2639 * Vector<E> r = this.rearrange(s.wrapIndexes());
2640 * VectorMask<E> valid = s.laneIsValid();
2641 * if (m.andNot(valid).anyTrue()) throw ...;
2642 * return broadcast(0).blend(r, m);
2643 * }</pre>
2644 *
2645 * @param s the shuffle controlling lane index selection
2646 * @param m the mask controlling application of the shuffle
2647 * @return the rearrangement of the lane elements of this vector
2648 * @throws IndexOutOfBoundsException if there are any exceptional
2649 * source indexes in the shuffle where the mask is set
2650 * @see #rearrange(VectorShuffle)
2651 * @see #rearrange(VectorShuffle,Vector)
2652 * @see VectorShuffle#laneIsValid()
2653 */
2654 public abstract Vector<E> rearrange(VectorShuffle<E> s, VectorMask<E> m);
2655
2656 /**
2657 * Rearranges the lane elements of two vectors, selecting lanes
2658 * under the control of a specific shuffle, using both normal and
2659 * exceptional indexes in the shuffle to steer data.
2660 *
2661 * This is a cross-lane operation that rearranges the lane
2662 * elements of the two input vectors (the current vector
2663 * and a second vector {@code v}).
2664 *
2665 * For each lane {@code N} of the shuffle, and for each lane
2666 * source index {@code I=s.laneSource(N)} in the shuffle,
2667 * the output lane {@code N} obtains the value from
2668 * the first vector at lane {@code I} if {@code I>=0}.
2669 * Otherwise, the exceptional index {@code I} is wrapped
2670 * by adding {@code VLENGTH} to it and used to index
2671 * the <em>second</em> vector, at index {@code I+VLENGTH}.
2672 *
2673 * <p> This method returns the value of this pseudocode:
2674 * <pre>{@code
2675 * Vector<E> r1 = this.rearrange(s.wrapIndexes());
2676 * // or else: r1 = this.rearrange(s, s.laneIsValid());
2677 * Vector<E> r2 = v.rearrange(s.wrapIndexes());
2678 * return r2.blend(r1,s.laneIsValid());
2679 * }</pre>
2680 *
2681 * @param s the shuffle controlling lane selection from both input vectors
2682 * @param v the second input vector
2683 * @return the rearrangement of lane elements of this vector and
2684 * a second input vector
2685 * @see #rearrange(VectorShuffle)
2686 * @see #rearrange(VectorShuffle,VectorMask)
2687 * @see VectorShuffle#laneIsValid()
2688 * @see #slice(int,Vector)
2689 */
2690 public abstract Vector<E> rearrange(VectorShuffle<E> s, Vector<E> v);
2691
2692 /**
2693 * Using index values stored in the lanes of this vector,
2694 * assemble values stored in second vector {@code v}.
2695 * The second vector thus serves as a table, whose
2696 * elements are selected by indexes in the current vector.
2697 *
2698 * This is a cross-lane operation that rearranges the lane
2699 * elements of the argument vector, under the control of
2700 * this vector.
2701 *
2702 * For each lane {@code N} of this vector, and for each lane
2703 * value {@code I=this.lane(N)} in this vector,
2704 * the output lane {@code N} obtains the value from
2705 * the argument vector at lane {@code I}.
2706 *
2707 * In this way, the result contains only values stored in the
2708 * argument vector {@code v}, but presented in an order which
2709 * depends on the index values in {@code this}.
2710 *
2711 * The result is the same as the expression
2712 * {@code v.rearrange(this.toShuffle())}.
2713 *
2714 * @param v the vector supplying the result values
2715 * @return the rearrangement of the lane elements of {@code v}
2716 * @throws IndexOutOfBoundsException if any invalid
2717 * source indexes are found in {@code this}
2718 * @see #rearrange(VectorShuffle)
2719 */
2720 public abstract Vector<E> selectFrom(Vector<E> v);
2721
2722 /**
2723 * Using index values stored in the lanes of this vector,
2724 * assemble values stored in second vector, under the control
2725 * of a mask.
2726 * Using index values stored in the lanes of this vector,
2727 * assemble values stored in second vector {@code v}.
2728 * The second vector thus serves as a table, whose
2729 * elements are selected by indexes in the current vector.
2730 * Lanes that are unset in the mask receive a
2731 * zero rather than a value from the table.
2732 *
2733 * This is a cross-lane operation that rearranges the lane
2734 * elements of the argument vector, under the control of
2735 * this vector and the mask.
2736 *
2737 * The result is the same as the expression
2738 * {@code v.rearrange(this.toShuffle(), m)}.
2739 *
2740 * @param v the vector supplying the result values
2741 * @param m the mask controlling selection from {@code v}
2742 * @return the rearrangement of the lane elements of {@code v}
2743 * @throws IndexOutOfBoundsException if any invalid
2744 * source indexes are found in {@code this},
2745 * in a lane which is set in the mask
2746 * @see #selectFrom(Vector)
2747 * @see #rearrange(VectorShuffle,VectorMask)
2748 */
2749 public abstract Vector<E> selectFrom(Vector<E> v, VectorMask<E> m);
2750
2751 // Conversions
2752
2753 /**
2754 * Returns a vector of the same species as this one
2755 * where all lane elements are set to
2756 * the primitive value {@code e}.
2757 *
2758 * The contents of the current vector are discarded;
2759 * only the species is relevant to this operation.
2760 *
2761 * <p> This method returns the value of this expression:
2762 * {@code EVector.broadcast(this.species(), (ETYPE)e)}, where
2763 * {@code EVector} is the vector class specific to this
2764 * vector's element type {@code ETYPE}.
2765 *
2766 * <p>
2767 * The {@code long} value {@code e} must be accurately
2768 * representable by the {@code ETYPE} of this vector's species,
2769 * so that {@code e==(long)(ETYPE)e}.
2770 *
2771 * If this rule is violated the problem is not detected
2772 * statically, but an {@code IllegalArgumentException} is thrown
2773 * at run-time. Thus, this method somewhat weakens the static
2774 * type checking of immediate constants and other scalars, but it
2775 * makes up for this by improving the expressiveness of the
2776 * generic API. Note that an {@code e} value in the range
2777 * {@code [-128..127]} is always acceptable, since every
2778 * {@code ETYPE} will accept every {@code byte} value.
2779 *
2780 * @apiNote
2781 * Subtypes improve on this method by sharpening
2782 * the method return type and
2783 * and the type of the scalar parameter {@code e}.
2784 *
2785 * @param e the value to broadcast
2786 * @return a vector where all lane elements are set to
2787 * the primitive value {@code e}
2788 * @throws IllegalArgumentException
2789 * if the given {@code long} value cannot
2790 * be represented by the vector's {@code ETYPE}
2791 * @see VectorSpecies#broadcast(long)
2792 * @see IntVector#broadcast(int)
2793 * @see FloatVector#broadcast(float)
2794 */
2795 public abstract Vector<E> broadcast(long e);
2796
2797 /**
2798 * Returns a mask of same species as this vector,
2799 * where each lane is set or unset according to given
2800 * single boolean, which is broadcast to all lanes.
2801 * <p>
2802 * This method returns the value of this expression:
2803 * {@code species().maskAll(bit)}.
2804 *
2805 * @param bit the given mask bit to be replicated
2806 * @return a mask where each lane is set or unset according to
2807 * the given bit
2808 * @see VectorSpecies#maskAll(boolean)
2809 */
2810 public abstract VectorMask<E> maskAll(boolean bit);
2811
2812 /**
2813 * Converts this vector into a shuffle, converting the lane values
2814 * to {@code int} and regarding them as source indexes.
2815 * <p>
2816 * This method behaves as if it returns the result of creating a shuffle
2817 * given an array of the vector elements, as follows:
2818 * <pre>{@code
2819 * long[] a = this.toLongArray();
2820 * int[] sa = new int[a.length];
2821 * for (int i = 0; i < a.length; i++) {
2822 * sa[i] = (int) a[i];
2823 * }
2824 * return VectorShuffle.fromValues(this.species(), sa);
2825 * }</pre>
2826 *
2827 * @return a shuffle representation of this vector
2828 * @see VectorShuffle#fromValues(VectorSpecies,int...)
2829 */
2830 public abstract VectorShuffle<E> toShuffle();
2831
2832 // Bitwise preserving
2833
2834 /**
2835 * Transforms this vector to a vector of the given species of
2836 * element type {@code F}, reinterpreting the bytes of this
2837 * vector without performing any value conversions.
2838 *
2839 * <p> Depending on the selected species, this operation may
2840 * either <a href="Vector.html#expansion">expand or contract</a>
2841 * its logical result, in which case a non-zero {@code part}
2842 * number can further control the selection and steering of the
2843 * logical result into the physical output vector.
2844 *
2845 * <p>
2846 * The underlying bits of this vector are copied to the resulting
2847 * vector without modification, but those bits, before copying,
2848 * may be truncated if the this vector's bit-size is greater than
2849 * desired vector's bit size, or filled with zero bits if this
2850 * vector's bit-size is less than desired vector's bit-size.
2851 *
2852 * <p> If the old and new species have different shape, this is a
2853 * <em>shape-changing</em> operation, and may have special
2854 * implementation costs.
2855 *
2856 * <p> The method behaves as if this vector is stored into a byte
2857 * buffer or array using little-endian byte ordering and then the
2858 * desired vector is loaded from the same byte buffer or array
2859 * using the same ordering.
2860 *
2861 * <p> The following pseudocode illustrates the behavior:
2862 * <pre>{@code
2863 * int domSize = this.byteSize();
2864 * int ranSize = species.vectorByteSize();
2865 * int M = (domSize > ranSize ? domSize / ranSize : ranSize / domSize);
2866 * assert Math.abs(part) < M;
2867 * assert (part == 0) || (part > 0) == (domSize > ranSize);
2868 * byte[] ra = new byte[Math.max(domSize, ranSize)];
2869 * if (domSize > ranSize) { // expansion
2870 * this.intoByteArray(ra, 0, ByteOrder.native());
2871 * int origin = part * ranSize;
2872 * return species.fromByteArray(ra, origin, ByteOrder.native());
2873 * } else { // contraction or size-invariant
2874 * int origin = (-part) * domSize;
2875 * this.intoByteArray(ra, origin, ByteOrder.native());
2876 * return species.fromByteArray(ra, 0, ByteOrder.native());
2877 * }
2878 * }</pre>
2879 *
2880 * @apiNote Although this method is defined as if the vectors in
2881 * question were loaded or stored into memory, memory semantics
2882 * has little to do or nothing with the actual implementation.
2883 * The appeal to little-endian ordering is simply a shorthand
2884 * for what could otherwise be a large number of detailed rules
2885 * concerning the mapping between lane-structured vectors and
2886 * byte-structured vectors.
2887 *
2888 * @param species the desired vector species
2889 * @param part the <a href="Vector.html#expansion">part number</a>
2890 * of the result, or zero if neither expanding nor contracting
2891 * @param <F> the boxed element type of the species
2892 * @return a vector transformed, by shape and element type, from this vector
2893 * @see Vector#convertShape(VectorOperators.Conversion,VectorSpecies,int)
2894 * @see Vector#castShape(VectorSpecies,int)
2895 * @see VectorSpecies#partLimit(VectorSpecies,boolean)
2896 */
2897 public abstract <F> Vector<F> reinterpretShape(VectorSpecies<F> species, int part);
2898
2899 /**
2900 * Views this vector as a vector of the same shape
2901 * and contents but a lane type of {@code byte},
2902 * where the bytes are extracted from the lanes
2903 * according to little-endian order.
2904 * It is a convenience method for the expression
2905 * {@code reinterpretShape(species().withLanes(byte.class))}.
2906 * It may be considered an inverse to the various
2907 * methods which consolidate bytes into larger lanes
2908 * within the same vector, such as
2909 * {@link Vector#reinterpretAsInts()}.
2910 *
2911 * @return a {@code ByteVector} with the same shape and information content
2912 * @see Vector#reinterpretShape(VectorSpecies,int)
2913 * @see IntVector#intoByteArray(byte[], int, ByteOrder)
2914 * @see FloatVector#intoByteArray(byte[], int, ByteOrder)
2915 * @see VectorSpecies#withLanes(Class)
2916 */
2917 public abstract ByteVector reinterpretAsBytes();
2918
2919 /**
2920 * Reinterprets this vector as a vector of the same shape
2921 * and contents but a lane type of {@code short},
2922 * where the lanes are assembled from successive bytes
2923 * according to little-endian order.
2924 * It is a convenience method for the expression
2925 * {@code reinterpretShape(species().withLanes(short.class))}.
2926 * It may be considered an inverse to {@link Vector#reinterpretAsBytes()}.
2927 *
2928 * @return a {@code ShortVector} with the same shape and information content
2929 */
2930 public abstract ShortVector reinterpretAsShorts();
2931
2932 /**
2933 * Reinterprets this vector as a vector of the same shape
2934 * and contents but a lane type of {@code int},
2935 * where the lanes are assembled from successive bytes
2936 * according to little-endian order.
2937 * It is a convenience method for the expression
2938 * {@code reinterpretShape(species().withLanes(int.class))}.
2939 * It may be considered an inverse to {@link Vector#reinterpretAsBytes()}.
2940 *
2941 * @return a {@code IntVector} with the same shape and information content
2942 */
2943 public abstract IntVector reinterpretAsInts();
2944
2945 /**
2946 * Reinterprets this vector as a vector of the same shape
2947 * and contents but a lane type of {@code long},
2948 * where the lanes are assembled from successive bytes
2949 * according to little-endian order.
2950 * It is a convenience method for the expression
2951 * {@code reinterpretShape(species().withLanes(long.class))}.
2952 * It may be considered an inverse to {@link Vector#reinterpretAsBytes()}.
2953 *
2954 * @return a {@code LongVector} with the same shape and information content
2955 */
2956 public abstract LongVector reinterpretAsLongs();
2957
2958 /**
2959 * Reinterprets this vector as a vector of the same shape
2960 * and contents but a lane type of {@code float},
2961 * where the lanes are assembled from successive bytes
2962 * according to little-endian order.
2963 * It is a convenience method for the expression
2964 * {@code reinterpretShape(species().withLanes(float.class))}.
2965 * It may be considered an inverse to {@link Vector#reinterpretAsBytes()}.
2966 *
2967 * @return a {@code FloatVector} with the same shape and information content
2968 */
2969 public abstract FloatVector reinterpretAsFloats();
2970
2971 /**
2972 * Reinterprets this vector as a vector of the same shape
2973 * and contents but a lane type of {@code double},
2974 * where the lanes are assembled from successive bytes
2975 * according to little-endian order.
2976 * It is a convenience method for the expression
2977 * {@code reinterpretShape(species().withLanes(double.class))}.
2978 * It may be considered an inverse to {@link Vector#reinterpretAsBytes()}.
2979 *
2980 * @return a {@code DoubleVector} with the same shape and information content
2981 */
2982 public abstract DoubleVector reinterpretAsDoubles();
2983
2984 /**
2985 * Views this vector as a vector of the same shape, length, and
2986 * contents, but a lane type that is not a floating-point type.
2987 *
2988 * This is a lane-wise reinterpretation cast on the lane values.
2989 * As such, this method does not change {@code VSHAPE} or
2990 * {@code VLENGTH}, and there is no change to the bitwise contents
2991 * of the vector. If the vector's {@code ETYPE} is already an
2992 * integral type, the same vector is returned unchanged.
2993 *
2994 * This method returns the value of this expression:
2995 * {@code convert(conv,0)}, where {@code conv} is
2996 * {@code VectorOperators.Conversion.ofReinterpret(E.class,F.class)},
2997 * and {@code F} is the non-floating-point type of the
2998 * same size as {@code E}.
2999 *
3000 * @apiNote
3001 * Subtypes improve on this method by sharpening
3002 * the return type.
3003 *
3004 * @return the original vector, reinterpreted as non-floating point
3005 * @see VectorOperators.Conversion#ofReinterpret(Class,Class)
3006 * @see Vector#convert(VectorOperators.Conversion,int)
3007 */
3008 public abstract Vector<?> viewAsIntegralLanes();
3009
3010 /**
3011 * Views this vector as a vector of the same shape, length, and
3012 * contents, but a lane type that is a floating-point type.
3013 *
3014 * This is a lane-wise reinterpretation cast on the lane values.
3015 * As such, there this method does not change {@code VSHAPE} or
3016 * {@code VLENGTH}, and there is no change to the bitwise contents
3017 * of the vector. If the vector's {@code ETYPE} is already a
3018 * float-point type, the same vector is returned unchanged.
3019 *
3020 * If the vector's element size does not match any floating point
3021 * type size, an {@code IllegalArgumentException} is thrown.
3022 *
3023 * This method returns the value of this expression:
3024 * {@code convert(conv,0)}, where {@code conv} is
3025 * {@code VectorOperators.Conversion.ofReinterpret(E.class,F.class)},
3026 * and {@code F} is the floating-point type of the
3027 * same size as {@code E}, if any.
3028 *
3029 * @apiNote
3030 * Subtypes improve on this method by sharpening
3031 * the return type.
3032 *
3033 * @return the original vector, reinterpreted as floating point
3034 * @throws UnsupportedOperationException if there is no floating point
3035 * type the same size as the lanes of this vector
3036 * @see VectorOperators.Conversion#ofReinterpret(Class,Class)
3037 * @see Vector#convert(VectorOperators.Conversion,int)
3038 */
3039 public abstract Vector<?> viewAsFloatingLanes();
3040
3041 /**
3042 * Convert this vector to a vector of the same shape and a new
3043 * element type, converting lane values from the current {@code ETYPE}
3044 * to a new lane type (called {@code FTYPE} here) according to the
3045 * indicated {@linkplain VectorOperators.Conversion conversion}.
3046 *
3047 * This is a lane-wise shape-invariant operation which copies
3048 * {@code ETYPE} values from the input vector to corresponding
3049 * {@code FTYPE} values in the result. Depending on the selected
3050 * conversion, this operation may either
3051 * <a href="Vector.html#expansion">expand or contract</a> its
3052 * logical result, in which case a non-zero {@code part} number
3053 * can further control the selection and steering of the logical
3054 * result into the physical output vector.
3055 *
3056 * <p> Each specific conversion is described by a conversion
3057 * constant in the class {@link VectorOperators}. Each conversion
3058 * operator has a specified {@linkplain
3059 * VectorOperators.Conversion#domainType() domain type} and
3060 * {@linkplain VectorOperators.Conversion#rangeType() range type}.
3061 * The domain type must exactly match the lane type of the input
3062 * vector, while the range type determines the lane type of the
3063 * output vectors.
3064 *
3065 * <p> A conversion operator may be classified as (respectively)
3066 * in-place, expanding, or contracting, depending on whether the
3067 * bit-size of its domain type is (respectively) equal, less than,
3068 * or greater than the bit-size of its range type.
3069 *
3070 * <p> Independently, conversion operations can also be classified
3071 * as reinterpreting or value-transforming, depending on whether
3072 * the conversion copies representation bits unchanged, or changes
3073 * the representation bits in order to retain (part or all of)
3074 * the logical value of the input value.
3075 *
3076 * <p> If a reinterpreting conversion contracts, it will truncate the
3077 * upper bits of the input. If it expands, it will pad upper bits
3078 * of the output with zero bits, when there are no corresponding
3079 * input bits.
3080 *
3081 * <p> An expanding conversion such as {@code S2I} ({@code short}
3082 * value to {@code int}) takes a scalar value and represents it
3083 * in a larger format (always with some information redundancy).
3084 *
3085 * A contracting conversion such as {@code D2F} ({@code double}
3086 * value to {@code float}) takes a scalar value and represents it
3087 * in a smaller format (always with some information loss).
3088 *
3089 * Some in-place conversions may also include information loss,
3090 * such as {@code L2D} ({@code long} value to {@code double})
3091 * or {@code F2I} ({@code float} value to {@code int}).
3092 *
3093 * Reinterpreting in-place conversions are not lossy, unless the
3094 * bitwise value is somehow not legal in the output type.
3095 * Converting the bit-pattern of a {@code NaN} may discard bits
3096 * from the {@code NaN}'s significand.
3097 *
3098 * <p> This classification is important, because, unless otherwise
3099 * documented, conversion operations <em>never change vector
3100 * shape</em>, regardless of how they may change <em>lane sizes</em>.
3101 *
3102 * Therefore an <em>expanding</em> conversion cannot store all of its
3103 * results in its output vector, because the output vector has fewer
3104 * lanes of larger size, in order to have the same overall bit-size as
3105 * its input.
3106 *
3107 * Likewise, a contracting conversion must store its relatively small
3108 * results into a subset of the lanes of the output vector, defaulting
3109 * the unused lanes to zero.
3110 *
3111 * <p> As an example, a conversion from {@code byte} to {@code long}
3112 * ({@code M=8}) will discard 87.5% of the input values in order to
3113 * convert the remaining 12.5% into the roomy {@code long} lanes of
3114 * the output vector. The inverse conversion will convert back all of
3115 * the large results, but will waste 87.5% of the lanes in the output
3116 * vector.
3117 *
3118 * <em>In-place</em> conversions ({@code M=1}) deliver all of
3119 * their results in one output vector, without wasting lanes.
3120 *
3121 * <p> To manage the details of these
3122 * <a href="Vector.html#expansion">expansions and contractions</a>,
3123 * a non-zero {@code part} parameter selects partial results from
3124 * expansions, or steers the results of contractions into
3125 * corresponding locations, as follows:
3126 *
3127 * <ul>
3128 * <li> expanding by {@code M}: {@code part} must be in the range
3129 * {@code [0..M-1]}, and selects the block of {@code VLENGTH/M} input
3130 * lanes starting at the <em>origin lane</em> at {@code part*VLENGTH/M}.
3131
3132 * <p> The {@code VLENGTH/M} output lanes represent a partial
3133 * slice of the whole logical result of the conversion, filling
3134 * the entire physical output vector.
3135 *
3136 * <li> contracting by {@code M}: {@code part} must be in the range
3137 * {@code [-M+1..0]}, and steers all {@code VLENGTH} input lanes into
3138 * the output located at the <em>origin lane</em> {@code -part*VLENGTH}.
3139 * There is a total of {@code VLENGTH*M} output lanes, and those not
3140 * holding converted input values are filled with zeroes.
3141 *
3142 * <p> A group of such output vectors, with logical result parts
3143 * steered to disjoint blocks, can be reassembled using the
3144 * {@linkplain VectorOperators#OR bitwise or} or (for floating
3145 * point) the {@link VectorOperators#FIRST_NONZERO FIRST_NONZERO}
3146 * operator.
3147 *
3148 * <li> in-place ({@code M=1}): {@code part} must be zero.
3149 * Both vectors have the same {@code VLENGTH}. The result is
3150 * always positioned at the <em>origin lane</em> of zero.
3151 *
3152 * </ul>
3153 *
3154 * <p> This method is a restricted version of the more general
3155 * but less frequently used <em>shape-changing</em> method
3156 * {@link #convertShape(VectorOperators.Conversion,VectorSpecies,int)
3157 * convertShape()}.
3158 * The result of this method is the same as the expression
3159 * {@code this.convertShape(conv, rsp, this.broadcast(part))},
3160 * where the output species is
3161 * {@code rsp=this.species().withLanes(FTYPE.class)}.
3162 *
3163 * @param conv the desired scalar conversion to apply lane-wise
3164 * @param part the <a href="Vector.html#expansion">part number</a>
3165 * of the result, or zero if neither expanding nor contracting
3166 * @param <F> the boxed element type of the species
3167 * @return a vector converted by shape and element type from this vector
3168 * @throws ArrayIndexOutOfBoundsException unless {@code part} is zero,
3169 * or else the expansion ratio is {@code M} and
3170 * {@code part} is positive and less than {@code M},
3171 * or else the contraction ratio is {@code M} and
3172 * {@code part} is negative and greater {@code -M}
3173 *
3174 * @see VectorOperators#I2L
3175 * @see VectorOperators.Conversion#ofCast(Class,Class)
3176 * @see VectorSpecies#partLimit(VectorSpecies,boolean)
3177 * @see #viewAsFloatingLanes()
3178 * @see #viewAsIntegralLanes()
3179 * @see #convertShape(VectorOperators.Conversion,VectorSpecies,int)
3180 * @see #reinterpretShape(VectorSpecies,int)
3181 */
3182 public abstract <F> Vector<F> convert(VectorOperators.Conversion<E,F> conv, int part);
3183
3184 /**
3185 * Converts this vector to a vector of the given species, shape and
3186 * element type, converting lane values from the current {@code ETYPE}
3187 * to a new lane type (called {@code FTYPE} here) according to the
3188 * indicated {@linkplain VectorOperators.Conversion conversion}.
3189 *
3190 * This is a lane-wise operation which copies {@code ETYPE} values
3191 * from the input vector to corresponding {@code FTYPE} values in
3192 * the result.
3193 *
3194 * <p> If the old and new species have the same shape, the behavior
3195 * is exactly the same as the simpler, shape-invariant method
3196 * {@link #convert(VectorOperators.Conversion,int) convert()}.
3197 * In such cases, the simpler method {@code convert()} should be
3198 * used, to make code easier to reason about.
3199 * Otherwise, this is a <em>shape-changing</em> operation, and may
3200 * have special implementation costs.
3201 *
3202 * <p> As a combined effect of shape changes and lane size changes,
3203 * the input and output species may have different lane counts, causing
3204 * <a href="Vector.html#expansion">expansion or contraction</a>.
3205 * In this case a non-zero {@code part} parameter selects
3206 * partial results from an expanded logical result, or steers
3207 * the results of a contracted logical result into a physical
3208 * output vector of the required output species.
3209 *
3210 * <p >The following pseudocode illustrates the behavior of this
3211 * method for in-place, expanding, and contracting conversions.
3212 * (This pseudocode also applies to the shape-invariant method,
3213 * but with shape restrictions on the output species.)
3214 * Note that only one of the three code paths is relevant to any
3215 * particular combination of conversion operator and shapes.
3216 *
3217 * <pre>{@code
3218 * FTYPE scalar_conversion_op(ETYPE s);
3219 * EVector a = ...;
3220 * VectorSpecies<F> rsp = ...;
3221 * int part = ...;
3222 * VectorSpecies<E> dsp = a.species();
3223 * int domlen = dsp.length();
3224 * int ranlen = rsp.length();
3225 * FTYPE[] logical = new FTYPE[domlen];
3226 * for (int i = 0; i < domlen; i++) {
3227 * logical[i] = scalar_conversion_op(a.lane(i));
3228 * }
3229 * FTYPE[] physical;
3230 * if (domlen == ranlen) { // in-place
3231 * assert part == 0; //else AIOOBE
3232 * physical = logical;
3233 * } else if (domlen > ranlen) { // expanding
3234 * int M = domlen / ranlen;
3235 * assert 0 <= part && part < M; //else AIOOBE
3236 * int origin = part * ranlen;
3237 * physical = Arrays.copyOfRange(logical, origin, origin + ranlen);
3238 * } else { // (domlen < ranlen) // contracting
3239 * int M = ranlen / domlen;
3240 * assert 0 >= part && part > -M; //else AIOOBE
3241 * int origin = -part * domlen;
3242 * System.arraycopy(logical, 0, physical, origin, domlen);
3243 * }
3244 * return FVector.fromArray(ran, physical, 0);
3245 * }</pre>
3246 *
3247 * @param conv the desired scalar conversion to apply lane-wise
3248 * @param rsp the desired output species
3249 * @param part the <a href="Vector.html#expansion">part number</a>
3250 * of the result, or zero if neither expanding nor contracting
3251 * @param <F> the boxed element type of the output species
3252 * @return a vector converted by element type from this vector
3253 * @see #convert(VectorOperators.Conversion,int)
3254 * @see #castShape(VectorSpecies,int)
3255 * @see #reinterpretShape(VectorSpecies,int)
3256 */
3257 public abstract <F> Vector<F> convertShape(VectorOperators.Conversion<E,F> conv, VectorSpecies<F> rsp, int part);
3258
3259 /**
3260 * Convenience method for converting a vector from one lane type
3261 * to another, reshaping as needed when lane sizes change.
3262 *
3263 * This method returns the value of this expression:
3264 * {@code convertShape(conv,rsp,part)}, where {@code conv} is
3265 * {@code VectorOperators.Conversion.ofCast(E.class,F.class)}.
3266 *
3267 * <p> If the old and new species have different shape, this is a
3268 * <em>shape-changing</em> operation, and may have special
3269 * implementation costs.
3270 *
3271 * @param rsp the desired output species
3272 * @param part the <a href="Vector.html#expansion">part number</a>
3273 * of the result, or zero if neither expanding nor contracting
3274 * @param <F> the boxed element type of the output species
3275 * @return a vector converted by element type from this vector
3276 * @see VectorOperators.Conversion#ofCast(Class,Class)
3277 * @see Vector#convertShape(VectorOperators.Conversion,VectorSpecies,int)
3278 */
3279 // Does this carry its weight?
3280 public abstract <F> Vector<F> castShape(VectorSpecies<F> rsp, int part);
3281
3282 /**
3283 * Checks that this vector has the given element type,
3284 * and returns this vector unchanged.
3285 * The effect is similar to this pseudocode:
3286 * {@code elementType == species().elementType()
3287 * ? this
3288 * : throw new ClassCastException()}.
3289 *
3290 * @param elementType the required lane type
3291 * @param <F> the boxed element type of the required lane type
3292 * @return the same vector
3293 * @throws ClassCastException if the vector has the wrong element type
3294 * @see VectorSpecies#check(Class)
3295 * @see VectorMask#check(Class)
3296 * @see Vector#check(VectorSpecies)
3297 * @see VectorShuffle#check(VectorSpecies)
3298 */
3299 public abstract <F> Vector<F> check(Class<F> elementType);
3300
3301 /**
3302 * Checks that this vector has the given species,
3303 * and returns this vector unchanged.
3304 * The effect is similar to this pseudocode:
3305 * {@code species == species()
3306 * ? this
3307 * : throw new ClassCastException()}.
3308 *
3309 * @param species the required species
3310 * @param <F> the boxed element type of the required species
3311 * @return the same vector
3312 * @throws ClassCastException if the vector has the wrong species
3313 * @see Vector#check(Class)
3314 * @see VectorMask#check(VectorSpecies)
3315 * @see VectorShuffle#check(VectorSpecies)
3316 */
3317 public abstract <F> Vector<F> check(VectorSpecies<F> species);
3318
3319 //Array stores
3320
3321 /**
3322 * Stores this vector into a byte array starting at an offset
3323 * using explicit byte order.
3324 * <p>
3325 * Bytes are extracted from primitive lane elements according
3326 * to the specified byte ordering.
3327 * The lanes are stored according to their
3328 * <a href="Vector.html#lane-order">memory ordering</a>.
3329 * <p>
3330 * This method behaves as if it calls
3331 * {@link #intoByteBuffer(ByteBuffer,int,ByteOrder,VectorMask)
3332 * intoByteBuffer()} as follows:
3333 * <pre>{@code
3334 * var bb = ByteBuffer.wrap(a);
3335 * var m = maskAll(true);
3336 * intoByteBuffer(bb, offset, bo, m);
3337 * }</pre>
3338 *
3339 * @param a the byte array
3340 * @param offset the offset into the array
3341 * @param bo the intended byte order
3342 * @throws IndexOutOfBoundsException
3343 * if {@code offset+N*ESIZE < 0}
3344 * or {@code offset+(N+1)*ESIZE > a.length}
3345 * for any lane {@code N} in the vector
3346 */
3347 public abstract void intoByteArray(byte[] a, int offset,
3348 ByteOrder bo);
3349
3350 /**
3351 * Stores this vector into a byte array starting at an offset
3352 * using explicit byte order and a mask.
3353 * <p>
3354 * Bytes are extracted from primitive lane elements according
3355 * to the specified byte ordering.
3356 * The lanes are stored according to their
3357 * <a href="Vector.html#lane-order">memory ordering</a>.
3358 * <p>
3359 * This method behaves as if it calls
3360 * {@link #intoByteBuffer(ByteBuffer,int,ByteOrder,VectorMask)
3361 * intoByteBuffer()} as follows:
3362 * <pre>{@code
3363 * var bb = ByteBuffer.wrap(a);
3364 * intoByteBuffer(bb, offset, bo, m);
3365 * }</pre>
3366 *
3367 * @param a the byte array
3368 * @param offset the offset into the array
3369 * @param bo the intended byte order
3370 * @param m the mask controlling lane selection
3371 * @throws IndexOutOfBoundsException
3372 * if {@code offset+N*ESIZE < 0}
3373 * or {@code offset+(N+1)*ESIZE > a.length}
3374 * for any lane {@code N} in the vector
3375 * where the mask is set
3376 */
3377 public abstract void intoByteArray(byte[] a, int offset,
3378 ByteOrder bo,
3379 VectorMask<E> m);
3380
3381 /**
3382 * Stores this vector into a byte buffer starting at an offset
3383 * using explicit byte order.
3384 * <p>
3385 * Bytes are extracted from primitive lane elements according
3386 * to the specified byte ordering.
3387 * The lanes are stored according to their
3388 * <a href="Vector.html#lane-order">memory ordering</a>.
3389 * <p>
3390 * This method behaves as if it calls
3391 * {@link #intoByteBuffer(ByteBuffer,int,ByteOrder,VectorMask)
3392 * intoByteBuffer()} as follows:
3393 * <pre>{@code
3394 * var m = maskAll(true);
3395 * intoByteBuffer(bb, offset, bo, m);
3396 * }</pre>
3397 *
3398 * @param bb the byte buffer
3399 * @param offset the offset into the array
3400 * @param bo the intended byte order
3401 * @throws IndexOutOfBoundsException
3402 * if {@code offset+N*ESIZE < 0}
3403 * or {@code offset+(N+1)*ESIZE > bb.limit()}
3404 * for any lane {@code N} in the vector
3405 * @throws java.nio.ReadOnlyBufferException
3406 * if the byte buffer is read-only
3407 */
3408 public abstract void intoByteBuffer(ByteBuffer bb, int offset, ByteOrder bo);
3409
3410 /**
3411 * Stores this vector into a byte buffer starting at an offset
3412 * using explicit byte order and a mask.
3413 * <p>
3414 * Bytes are extracted from primitive lane elements according
3415 * to the specified byte ordering.
3416 * The lanes are stored according to their
3417 * <a href="Vector.html#lane-order">memory ordering</a>.
3418 * <p>
3419 * The following pseudocode illustrates the behavior, where
3420 * the primitive element type is not of {@code byte},
3421 * {@code EBuffer} is the primitive buffer type, {@code ETYPE} is the
3422 * primitive element type, and {@code EVector} is the primitive
3423 * vector type for this vector:
3424 * <pre>{@code
3425 * EBuffer eb = bb.duplicate()
3426 * .position(offset)
3427 * .order(bo).asEBuffer();
3428 * ETYPE[] a = this.toArray();
3429 * for (int n = 0; n < a.length; n++) {
3430 * if (m.laneIsSet(n)) {
3431 * eb.put(n, a[n]);
3432 * }
3433 * }
3434 * }</pre>
3435 * When the primitive element type is of {@code byte} the primitive
3436 * byte buffer is obtained as follows, where operation on the buffer
3437 * remains the same as in the prior pseudocode:
3438 * <pre>{@code
3439 * ByteBuffer eb = bb.duplicate()
3440 * .position(offset);
3441 * }</pre>
3442 *
3443 * @implNote
3444 * This operation is likely to be more efficient if
3445 * the specified byte order is the same as
3446 * {@linkplain ByteOrder#nativeOrder()
3447 * the platform native order},
3448 * since this method will not need to reorder
3449 * the bytes of lane values.
3450 * In the special case where {@code ETYPE} is
3451 * {@code byte}, the byte order argument is
3452 * ignored.
3453 *
3454 * @param bb the byte buffer
3455 * @param offset the offset into the array
3456 * @param bo the intended byte order
3457 * @param m the mask controlling lane selection
3458 * @throws IndexOutOfBoundsException
3459 * if {@code offset+N*ESIZE < 0}
3460 * or {@code offset+(N+1)*ESIZE > bb.limit()}
3461 * for any lane {@code N} in the vector
3462 * where the mask is set
3463 * @throws java.nio.ReadOnlyBufferException
3464 * if the byte buffer is read-only
3465 */
3466 public abstract void intoByteBuffer(ByteBuffer bb, int offset,
3467 ByteOrder bo, VectorMask<E> m);
3468
3469 /**
3470 * Returns a packed array containing all the lane values.
3471 * The array length is the same as the vector length.
3472 * The element type of the array is the same as the element
3473 * type of the vector.
3474 * The array elements are stored in lane order.
3475 * Overrides of this method on subtypes of {@code Vector}
3476 * which specify the element type have an accurately typed
3477 * array result.
3478 *
3479 * @apiNote
3480 * Usually {@linkplain FloatVector#toArray() strongly typed access}
3481 * is preferable, if you are working with a vector
3482 * subtype that has a known element type.
3483 *
3484 * @return an accurately typed array containing
3485 * the lane values of this vector
3486 * @see ByteVector#toArray()
3487 * @see IntVector#toArray()
3488 * @see DoubleVector#toArray()
3489 */
3490 public abstract Object toArray();
3491
3492 /**
3493 * Returns an {@code int[]} array containing all
3494 * the lane values, converted to the type {@code int}.
3495 * The array length is the same as the vector length.
3496 * The array elements are converted as if by casting
3497 * and stored in lane order.
3498 *
3499 * This operation may fail if the vector element type is {@code
3500 * float} or {@code double}, when lanes contain fractional or
3501 * out-of-range values. If any vector lane value is not
3502 * representable as an {@code int}, an exception is thrown.
3503 *
3504 * @apiNote
3505 * Usually {@linkplain FloatVector#toArray() strongly typed access}
3506 * is preferable, if you are working with a vector
3507 * subtype that has a known element type.
3508 *
3509 * @return an {@code int[]} array containing
3510 * the lane values of this vector
3511 * @throws UnsupportedOperationException
3512 * if any lane value cannot be represented as an
3513 * {@code int} array element
3514 * @see #toArray()
3515 * @see #toLongArray()
3516 * @see #toDoubleArray()
3517 * @see IntVector#toArray()
3518 */
3519 public abstract int[] toIntArray();
3520
3521 /**
3522 * Returns a {@code long[]} array containing all
3523 * the lane values, converted to the type {@code long}.
3524 * The array length is the same as the vector length.
3525 * The array elements are converted as if by casting
3526 * and stored in lane order.
3527 *
3528 * This operation may fail if the vector element type is {@code
3529 * float} or {@code double}, when lanes contain fractional or
3530 * out-of-range values. If any vector lane value is not
3531 * representable as a {@code long}, an exception is thrown.
3532 *
3533 * @apiNote
3534 * Usually {@linkplain FloatVector#toArray() strongly typed access}
3535 * is preferable, if you are working with a vector
3536 * subtype that has a known element type.
3537 *
3538 * @return a {@code long[]} array containing
3539 * the lane values of this vector
3540 * @throws UnsupportedOperationException
3541 * if any lane value cannot be represented as a
3542 * {@code long} array element
3543 * @see #toArray()
3544 * @see #toIntArray()
3545 * @see #toDoubleArray()
3546 * @see LongVector#toArray()
3547 */
3548 public abstract long[] toLongArray();
3549
3550 /**
3551 * Returns a {@code double[]} array containing all
3552 * the lane values, converted to the type {@code double}.
3553 * The array length is the same as the vector length.
3554 * The array elements are converted as if by casting
3555 * and stored in lane order.
3556 * This operation can lose precision
3557 * if the vector element type is {@code long}.
3558 *
3559 * @apiNote
3560 * Usually {@link FloatVector#toArray() strongly typed access}
3561 * is preferable, if you are working with a vector
3562 * subtype that has a known element type.
3563 *
3564 * @return a {@code double[]} array containing
3565 * the lane values of this vector,
3566 * possibly rounded to representable
3567 * {@code double} values
3568 * @see #toArray()
3569 * @see #toIntArray()
3570 * @see #toLongArray()
3571 * @see DoubleVector#toArray()
3572 */
3573 public abstract double[] toDoubleArray();
3574
3575 /**
3576 * Returns a string representation of this vector, of the form
3577 * {@code "[0,1,2...]"}, reporting the lane values of this
3578 * vector, in lane order.
3579 *
3580 * The string is produced as if by a call to
3581 * {@link Arrays#toString(int[]) Arrays.toString()},
3582 * as appropriate to the array returned by
3583 * {@link #toArray() this.toArray()}.
3584 *
3585 * @return a string of the form {@code "[0,1,2...]"}
3586 * reporting the lane values of this vector
3587 */
3588 @Override
3589 public abstract String toString();
3590
3591 /**
3592 * Indicates whether this vector is identical to some other object.
3593 * Two vectors are identical only if they have the same species
3594 * and same lane values, in the same order.
3595 * <p>The comparison of lane values is produced as if by a call to
3596 * {@link Arrays#equals(int[],int[]) Arrays.equals()},
3597 * as appropriate to the arrays returned by
3598 * {@link #toArray toArray()} on both vectors.
3599 *
3600 * @return whether this vector is identical to some other object
3601 * @see #eq
3602 */
3603 @Override
3604 public abstract boolean equals(Object obj);
3605
3606 /**
3607 * Returns a hash code value for the vector.
3608 * based on the lane values and the vector species.
3609 *
3610 * @return a hash code value for this vector
3611 */
3612 @Override
3613 public abstract int hashCode();
3614
3615 // ==== JROSE NAME CHANGES ====
3616
3617 // RAISED FROM SUBCLASSES (with generalized type)
3618 // * toArray() -> ETYPE[] <: Object (erased return type for interop)
3619 // * toString(), equals(Object), hashCode() (documented)
3620 // ADDED
3621 // * compare(OP,v) to replace most of the comparison methods
3622 // * maskAll(boolean) to replace maskAllTrue/False
3623 // * toLongArray(), toDoubleArray() (generic unboxed access)
3624 // * check(Class), check(VectorSpecies) (static type-safety checks)
3625 // * enum Comparison (enum of EQ, NE, GT, LT, GE, LE)
3626 // * zero(VS), broadcast(long) (basic factories)
3627 // * reinterpretAsEs(), viewAsXLanes (bytewise reinterpreting views)
3628 // * addIndex(int) (iota function)
3629
3630 }