1 /*
2 * Copyright (c) 2018, 2019, Red Hat, Inc. All rights reserved.
3 * Copyright Amazon.com Inc. or its affiliates. All Rights Reserved.
4 * Copyright (c) 2025, Oracle and/or its affiliates. All rights reserved.
5 * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
6 *
7 * This code is free software; you can redistribute it and/or modify it
8 * under the terms of the GNU General Public License version 2 only, as
9 * published by the Free Software Foundation.
10 *
11 * This code is distributed in the hope that it will be useful, but WITHOUT
12 * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
13 * FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
14 * version 2 for more details (a copy is included in the LICENSE file that
15 * accompanied this code).
16 *
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19 * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
20 *
21 * Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA
22 * or visit www.oracle.com if you need additional information or have any
23 * questions.
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25 */
26
27
28 #include "gc/shared/gcCause.hpp"
29 #include "gc/shenandoah/heuristics/shenandoahAdaptiveHeuristics.hpp"
30 #include "gc/shenandoah/heuristics/shenandoahHeuristics.hpp"
31 #include "gc/shenandoah/heuristics/shenandoahSpaceInfo.hpp"
32 #include "gc/shenandoah/shenandoahCollectionSet.hpp"
33 #include "gc/shenandoah/shenandoahCollectorPolicy.hpp"
34 #include "gc/shenandoah/shenandoahFreeSet.hpp"
35 #include "gc/shenandoah/shenandoahHeap.inline.hpp"
36 #include "gc/shenandoah/shenandoahHeapRegion.inline.hpp"
37 #include "logging/log.hpp"
38 #include "logging/logTag.hpp"
39 #include "runtime/globals.hpp"
40 #include "utilities/quickSort.hpp"
41
42 // These constants are used to adjust the margin of error for the moving
43 // average of the allocation rate and cycle time. The units are standard
44 // deviations.
45 const double ShenandoahAdaptiveHeuristics::FULL_PENALTY_SD = 0.2;
46 const double ShenandoahAdaptiveHeuristics::DEGENERATE_PENALTY_SD = 0.1;
47
48 // These are used to decide if we want to make any adjustments at all
49 // at the end of a successful concurrent cycle.
50 const double ShenandoahAdaptiveHeuristics::LOWEST_EXPECTED_AVAILABLE_AT_END = -0.5;
51 const double ShenandoahAdaptiveHeuristics::HIGHEST_EXPECTED_AVAILABLE_AT_END = 0.5;
52
53 // These values are the confidence interval expressed as standard deviations.
54 // At the minimum confidence level, there is a 25% chance that the true value of
55 // the estimate (average cycle time or allocation rate) is not more than
56 // MINIMUM_CONFIDENCE standard deviations away from our estimate. Similarly, the
57 // MAXIMUM_CONFIDENCE interval here means there is a one in a thousand chance
58 // that the true value of our estimate is outside the interval. These are used
59 // as bounds on the adjustments applied at the outcome of a GC cycle.
60 const double ShenandoahAdaptiveHeuristics::MINIMUM_CONFIDENCE = 0.319; // 25%
61 const double ShenandoahAdaptiveHeuristics::MAXIMUM_CONFIDENCE = 3.291; // 99.9%
62
63 ShenandoahAdaptiveHeuristics::ShenandoahAdaptiveHeuristics(ShenandoahSpaceInfo* space_info) :
64 ShenandoahHeuristics(space_info),
65 _margin_of_error_sd(ShenandoahAdaptiveInitialConfidence),
66 _spike_threshold_sd(ShenandoahAdaptiveInitialSpikeThreshold),
67 _last_trigger(OTHER),
68 _available(Moving_Average_Samples, ShenandoahAdaptiveDecayFactor) { }
69
70 ShenandoahAdaptiveHeuristics::~ShenandoahAdaptiveHeuristics() {}
71
72 void ShenandoahAdaptiveHeuristics::choose_collection_set_from_regiondata(ShenandoahCollectionSet* cset,
73 RegionData* data, size_t size,
74 size_t actual_free) {
75 size_t garbage_threshold = ShenandoahHeapRegion::region_size_bytes() * ShenandoahGarbageThreshold / 100;
76
77 // The logic for cset selection in adaptive is as follows:
78 //
79 // 1. We cannot get cset larger than available free space. Otherwise we guarantee OOME
80 // during evacuation, and thus guarantee full GC. In practice, we also want to let
81 // application to allocate something. This is why we limit CSet to some fraction of
82 // available space. In non-overloaded heap, max_cset would contain all plausible candidates
83 // over garbage threshold.
84 //
85 // 2. We should not get cset too low so that free threshold would not be met right
86 // after the cycle. Otherwise we get back-to-back cycles for no reason if heap is
87 // too fragmented. In non-overloaded non-fragmented heap min_garbage would be around zero.
88 //
89 // Therefore, we start by sorting the regions by garbage. Then we unconditionally add the best candidates
90 // before we meet min_garbage. Then we add all candidates that fit with a garbage threshold before
91 // we hit max_cset. When max_cset is hit, we terminate the cset selection. Note that in this scheme,
92 // ShenandoahGarbageThreshold is the soft threshold which would be ignored until min_garbage is hit.
93
94 size_t capacity = ShenandoahHeap::heap()->soft_max_capacity();
95 size_t max_cset = (size_t)((1.0 * capacity / 100 * ShenandoahEvacReserve) / ShenandoahEvacWaste);
96 size_t free_target = (capacity / 100 * ShenandoahMinFreeThreshold) + max_cset;
97 size_t min_garbage = (free_target > actual_free ? (free_target - actual_free) : 0);
98
99 log_info(gc, ergo)("Adaptive CSet Selection. Target Free: %zu%s, Actual Free: "
100 "%zu%s, Max Evacuation: %zu%s, Min Garbage: %zu%s",
101 byte_size_in_proper_unit(free_target), proper_unit_for_byte_size(free_target),
102 byte_size_in_proper_unit(actual_free), proper_unit_for_byte_size(actual_free),
103 byte_size_in_proper_unit(max_cset), proper_unit_for_byte_size(max_cset),
104 byte_size_in_proper_unit(min_garbage), proper_unit_for_byte_size(min_garbage));
105
106 // Better select garbage-first regions
107 QuickSort::sort(data, size, compare_by_garbage);
108
109 size_t cur_cset = 0;
110 size_t cur_garbage = 0;
111
112 for (size_t idx = 0; idx < size; idx++) {
113 ShenandoahHeapRegion* r = data[idx].get_region();
114
115 size_t new_cset = cur_cset + r->get_live_data_bytes();
116 size_t new_garbage = cur_garbage + r->garbage();
117
118 if (new_cset > max_cset) {
119 break;
120 }
121
122 if ((new_garbage < min_garbage) || (r->garbage() > garbage_threshold)) {
123 cset->add_region(r);
124 cur_cset = new_cset;
125 cur_garbage = new_garbage;
126 }
127 }
128 }
129
130 void ShenandoahAdaptiveHeuristics::record_cycle_start() {
131 ShenandoahHeuristics::record_cycle_start();
132 _allocation_rate.allocation_counter_reset();
133 }
134
135 void ShenandoahAdaptiveHeuristics::record_success_concurrent() {
136 ShenandoahHeuristics::record_success_concurrent();
137
138 size_t available = _space_info->available();
139
140 double z_score = 0.0;
141 double available_sd = _available.sd();
142 if (available_sd > 0) {
143 double available_avg = _available.avg();
144 z_score = (double(available) - available_avg) / available_sd;
145 log_debug(gc, ergo)("Available: %zu %sB, z-score=%.3f. Average available: %.1f %sB +/- %.1f %sB.",
146 byte_size_in_proper_unit(available), proper_unit_for_byte_size(available),
147 z_score,
148 byte_size_in_proper_unit(available_avg), proper_unit_for_byte_size(available_avg),
149 byte_size_in_proper_unit(available_sd), proper_unit_for_byte_size(available_sd));
150 }
151
152 _available.add(double(available));
153
154 // In the case when a concurrent GC cycle completes successfully but with an
155 // unusually small amount of available memory we will adjust our trigger
156 // parameters so that they are more likely to initiate a new cycle.
157 // Conversely, when a GC cycle results in an above average amount of available
158 // memory, we will adjust the trigger parameters to be less likely to initiate
159 // a GC cycle.
160 //
161 // The z-score we've computed is in no way statistically related to the
162 // trigger parameters, but it has the nice property that worse z-scores for
163 // available memory indicate making larger adjustments to the trigger
164 // parameters. It also results in fewer adjustments as the application
165 // stabilizes.
166 //
167 // In order to avoid making endless and likely unnecessary adjustments to the
168 // trigger parameters, the change in available memory (with respect to the
169 // average) at the end of a cycle must be beyond these threshold values.
170 if (z_score < LOWEST_EXPECTED_AVAILABLE_AT_END ||
171 z_score > HIGHEST_EXPECTED_AVAILABLE_AT_END) {
172 // The sign is flipped because a negative z-score indicates that the
173 // available memory at the end of the cycle is below average. Positive
174 // adjustments make the triggers more sensitive (i.e., more likely to fire).
175 // The z-score also gives us a measure of just how far below normal. This
176 // property allows us to adjust the trigger parameters proportionally.
177 //
178 // The `100` here is used to attenuate the size of our adjustments. This
179 // number was chosen empirically. It also means the adjustments at the end of
180 // a concurrent cycle are an order of magnitude smaller than the adjustments
181 // made for a degenerated or full GC cycle (which themselves were also
182 // chosen empirically).
183 adjust_last_trigger_parameters(z_score / -100);
184 }
185 }
186
187 void ShenandoahAdaptiveHeuristics::record_success_degenerated() {
188 ShenandoahHeuristics::record_success_degenerated();
189 // Adjust both trigger's parameters in the case of a degenerated GC because
190 // either of them should have triggered earlier to avoid this case.
191 adjust_margin_of_error(DEGENERATE_PENALTY_SD);
192 adjust_spike_threshold(DEGENERATE_PENALTY_SD);
193 }
194
195 void ShenandoahAdaptiveHeuristics::record_success_full() {
196 ShenandoahHeuristics::record_success_full();
197 // Adjust both trigger's parameters in the case of a full GC because
198 // either of them should have triggered earlier to avoid this case.
199 adjust_margin_of_error(FULL_PENALTY_SD);
200 adjust_spike_threshold(FULL_PENALTY_SD);
201 }
202
203 static double saturate(double value, double min, double max) {
204 return MAX2(MIN2(value, max), min);
205 }
206
207 // Rationale:
208 // The idea is that there is an average allocation rate and there are occasional abnormal bursts (or spikes) of
209 // allocations that exceed the average allocation rate. What do these spikes look like?
210 //
211 // 1. At certain phase changes, we may discard large amounts of data and replace it with large numbers of newly
212 // allocated objects. This "spike" looks more like a phase change. We were in steady state at M bytes/sec
213 // allocation rate and now we're in a "reinitialization phase" that looks like N bytes/sec. We need the "spike"
214 // accommodation to give us enough runway to recalibrate our "average allocation rate".
215 //
216 // 2. The typical workload changes. "Suddenly", our typical workload of N TPS increases to N+delta TPS. This means
217 // our average allocation rate needs to be adjusted. Once again, we need the "spike" accomodation to give us
218 // enough runway to recalibrate our "average allocation rate".
219 //
220 // 3. Though there is an "average" allocation rate, a given workload's demand for allocation may be very bursty. We
221 // allocate a bunch of LABs during the 5 ms that follow completion of a GC, then we perform no more allocations for
222 // the next 150 ms. It seems we want the "spike" to represent the maximum divergence from average within the
223 // period of time between consecutive evaluation of the should_start_gc() service. Here's the thinking:
224 //
225 // a) Between now and the next time I ask whether should_start_gc(), we might experience a spike representing
226 // the anticipated burst of allocations. If that would put us over budget, then we should start GC immediately.
227 // b) Between now and the anticipated depletion of allocation pool, there may be two or more bursts of allocations.
228 // If there are more than one of these bursts, we can "approximate" that these will be separated by spans of
229 // time with very little or no allocations so the "average" allocation rate should be a suitable approximation
230 // of how this will behave.
231 //
232 // For cases 1 and 2, we need to "quickly" recalibrate the average allocation rate whenever we detect a change
233 // in operation mode. We want some way to decide that the average rate has changed, while keeping average
234 // allocation rate computation independent.
235 bool ShenandoahAdaptiveHeuristics::should_start_gc() {
236 size_t capacity = ShenandoahHeap::heap()->soft_max_capacity();
237 size_t available = _space_info->soft_available();
238 size_t allocated = _space_info->bytes_allocated_since_gc_start();
239
240 log_debug(gc)("should_start_gc? available: %zu, soft_max_capacity: %zu"
241 ", allocated: %zu", available, capacity, allocated);
242
243 if (_start_gc_is_pending) {
244 log_trigger("GC start is already pending");
245 return true;
246 }
247
248 // Track allocation rate even if we decide to start a cycle for other reasons.
249 double rate = _allocation_rate.sample(allocated);
250 _last_trigger = OTHER;
251
252 size_t min_threshold = min_free_threshold();
253 if (available < min_threshold) {
254 log_trigger("Free (%zu%s) is below minimum threshold (%zu%s)",
255 byte_size_in_proper_unit(available), proper_unit_for_byte_size(available),
256 byte_size_in_proper_unit(min_threshold), proper_unit_for_byte_size(min_threshold));
257 accept_trigger_with_type(OTHER);
258 return true;
259 }
260
261 // Check if we need to learn a bit about the application
262 const size_t max_learn = ShenandoahLearningSteps;
263 if (_gc_times_learned < max_learn) {
264 size_t init_threshold = capacity / 100 * ShenandoahInitFreeThreshold;
265 if (available < init_threshold) {
266 log_trigger("Learning %zu of %zu. Free (%zu%s) is below initial threshold (%zu%s)",
267 _gc_times_learned + 1, max_learn,
268 byte_size_in_proper_unit(available), proper_unit_for_byte_size(available),
269 byte_size_in_proper_unit(init_threshold), proper_unit_for_byte_size(init_threshold));
270 accept_trigger_with_type(OTHER);
271 return true;
272 }
273 }
274 // Check if allocation headroom is still okay. This also factors in:
275 // 1. Some space to absorb allocation spikes (ShenandoahAllocSpikeFactor)
276 // 2. Accumulated penalties from Degenerated and Full GC
277 size_t allocation_headroom = available;
278
279 size_t spike_headroom = capacity / 100 * ShenandoahAllocSpikeFactor;
280 size_t penalties = capacity / 100 * _gc_time_penalties;
281
282 allocation_headroom -= MIN2(allocation_headroom, spike_headroom);
283 allocation_headroom -= MIN2(allocation_headroom, penalties);
284
285 double avg_cycle_time = _gc_cycle_time_history->davg() + (_margin_of_error_sd * _gc_cycle_time_history->dsd());
286 double avg_alloc_rate = _allocation_rate.upper_bound(_margin_of_error_sd);
287
288 log_debug(gc)("average GC time: %.2f ms, allocation rate: %.0f %s/s",
289 avg_cycle_time * 1000, byte_size_in_proper_unit(avg_alloc_rate), proper_unit_for_byte_size(avg_alloc_rate));
290 if (avg_cycle_time * avg_alloc_rate > allocation_headroom) {
291 log_trigger("Average GC time (%.2f ms) is above the time for average allocation rate (%.0f %sB/s)"
292 " to deplete free headroom (%zu%s) (margin of error = %.2f)",
293 avg_cycle_time * 1000,
294 byte_size_in_proper_unit(avg_alloc_rate), proper_unit_for_byte_size(avg_alloc_rate),
295 byte_size_in_proper_unit(allocation_headroom), proper_unit_for_byte_size(allocation_headroom),
296 _margin_of_error_sd);
297 log_info(gc, ergo)("Free headroom: %zu%s (free) - %zu%s (spike) - %zu%s (penalties) = %zu%s",
298 byte_size_in_proper_unit(available), proper_unit_for_byte_size(available),
299 byte_size_in_proper_unit(spike_headroom), proper_unit_for_byte_size(spike_headroom),
300 byte_size_in_proper_unit(penalties), proper_unit_for_byte_size(penalties),
301 byte_size_in_proper_unit(allocation_headroom), proper_unit_for_byte_size(allocation_headroom));
302 accept_trigger_with_type(RATE);
303 return true;
304 }
305
306 bool is_spiking = _allocation_rate.is_spiking(rate, _spike_threshold_sd);
307 if (is_spiking && avg_cycle_time > allocation_headroom / rate) {
308 log_trigger("Average GC time (%.2f ms) is above the time for instantaneous allocation rate (%.0f %sB/s) to deplete free headroom (%zu%s) (spike threshold = %.2f)",
309 avg_cycle_time * 1000,
310 byte_size_in_proper_unit(rate), proper_unit_for_byte_size(rate),
311 byte_size_in_proper_unit(allocation_headroom), proper_unit_for_byte_size(allocation_headroom),
312 _spike_threshold_sd);
313 accept_trigger_with_type(SPIKE);
314 return true;
315 }
316
317 if (ShenandoahHeuristics::should_start_gc()) {
318 _start_gc_is_pending = true;
319 return true;
320 } else {
321 return false;
322 }
323 }
324
325 void ShenandoahAdaptiveHeuristics::adjust_last_trigger_parameters(double amount) {
326 switch (_last_trigger) {
327 case RATE:
328 adjust_margin_of_error(amount);
329 break;
330 case SPIKE:
331 adjust_spike_threshold(amount);
332 break;
333 case OTHER:
334 // nothing to adjust here.
335 break;
336 default:
337 ShouldNotReachHere();
338 }
339 }
340
341 void ShenandoahAdaptiveHeuristics::adjust_margin_of_error(double amount) {
342 _margin_of_error_sd = saturate(_margin_of_error_sd + amount, MINIMUM_CONFIDENCE, MAXIMUM_CONFIDENCE);
343 log_debug(gc, ergo)("Margin of error now %.2f", _margin_of_error_sd);
344 }
345
346 void ShenandoahAdaptiveHeuristics::adjust_spike_threshold(double amount) {
347 _spike_threshold_sd = saturate(_spike_threshold_sd - amount, MINIMUM_CONFIDENCE, MAXIMUM_CONFIDENCE);
348 log_debug(gc, ergo)("Spike threshold now: %.2f", _spike_threshold_sd);
349 }
350
351 size_t ShenandoahAdaptiveHeuristics::min_free_threshold() {
352 return ShenandoahHeap::heap()->soft_max_capacity() / 100 * ShenandoahMinFreeThreshold;
353 }
354
355 ShenandoahAllocationRate::ShenandoahAllocationRate() :
356 _last_sample_time(os::elapsedTime()),
357 _last_sample_value(0),
358 _interval_sec(1.0 / ShenandoahAdaptiveSampleFrequencyHz),
359 _rate(int(ShenandoahAdaptiveSampleSizeSeconds * ShenandoahAdaptiveSampleFrequencyHz), ShenandoahAdaptiveDecayFactor),
360 _rate_avg(int(ShenandoahAdaptiveSampleSizeSeconds * ShenandoahAdaptiveSampleFrequencyHz), ShenandoahAdaptiveDecayFactor) {
361 }
362
363 double ShenandoahAllocationRate::sample(size_t allocated) {
364 double now = os::elapsedTime();
365 double rate = 0.0;
366 if (now - _last_sample_time > _interval_sec) {
367 if (allocated >= _last_sample_value) {
368 rate = instantaneous_rate(now, allocated);
369 _rate.add(rate);
370 _rate_avg.add(_rate.avg());
371 }
372
373 _last_sample_time = now;
374 _last_sample_value = allocated;
375 }
376 return rate;
377 }
378
379 double ShenandoahAllocationRate::upper_bound(double sds) const {
380 // Here we are using the standard deviation of the computed running
381 // average, rather than the standard deviation of the samples that went
382 // into the moving average. This is a much more stable value and is tied
383 // to the actual statistic in use (moving average over samples of averages).
384 return _rate.davg() + (sds * _rate_avg.dsd());
385 }
386
387 void ShenandoahAllocationRate::allocation_counter_reset() {
388 _last_sample_time = os::elapsedTime();
389 _last_sample_value = 0;
390 }
391
392 bool ShenandoahAllocationRate::is_spiking(double rate, double threshold) const {
393 if (rate <= 0.0) {
394 return false;
395 }
396
397 double sd = _rate.sd();
398 if (sd > 0) {
399 // There is a small chance that that rate has already been sampled, but it
400 // seems not to matter in practice.
401 double z_score = (rate - _rate.avg()) / sd;
402 if (z_score > threshold) {
403 return true;
404 }
405 }
406 return false;
407 }
408
409 double ShenandoahAllocationRate::instantaneous_rate(double time, size_t allocated) const {
410 size_t last_value = _last_sample_value;
411 double last_time = _last_sample_time;
412 size_t allocation_delta = (allocated > last_value) ? (allocated - last_value) : 0;
413 double time_delta_sec = time - last_time;
414 return (time_delta_sec > 0) ? (allocation_delta / time_delta_sec) : 0;
415 }