1 /*
2 * Copyright (c) 2018, 2019, Red Hat, Inc. All rights reserved.
3 * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
4 *
5 * This code is free software; you can redistribute it and/or modify it
6 * under the terms of the GNU General Public License version 2 only, as
7 * published by the Free Software Foundation.
8 *
9 * This code is distributed in the hope that it will be useful, but WITHOUT
10 * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
11 * FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
12 * version 2 for more details (a copy is included in the LICENSE file that
13 * accompanied this code).
14 *
15 * You should have received a copy of the GNU General Public License version
16 * 2 along with this work; if not, write to the Free Software Foundation,
17 * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
18 *
19 * Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA
20 * or visit www.oracle.com if you need additional information or have any
21 * questions.
22 *
23 */
24
25 #include "precompiled.hpp"
26
27 #include "gc/shenandoah/heuristics/shenandoahAdaptiveHeuristics.hpp"
28 #include "gc/shenandoah/shenandoahCollectionSet.hpp"
29 #include "gc/shenandoah/shenandoahFreeSet.hpp"
30 #include "gc/shenandoah/shenandoahGeneration.hpp"
31 #include "gc/shenandoah/shenandoahHeapRegion.inline.hpp"
32 #include "logging/log.hpp"
33 #include "logging/logTag.hpp"
34 #include "utilities/quickSort.hpp"
35
36 // These constants are used to adjust the margin of error for the moving
37 // average of the allocation rate and cycle time. The units are standard
38 // deviations.
39 const double ShenandoahAdaptiveHeuristics::FULL_PENALTY_SD = 0.2;
40 const double ShenandoahAdaptiveHeuristics::DEGENERATE_PENALTY_SD = 0.1;
41
42 // These are used to decide if we want to make any adjustments at all
43 // at the end of a successful concurrent cycle.
44 const double ShenandoahAdaptiveHeuristics::LOWEST_EXPECTED_AVAILABLE_AT_END = -0.5;
45 const double ShenandoahAdaptiveHeuristics::HIGHEST_EXPECTED_AVAILABLE_AT_END = 0.5;
46
47 // These values are the confidence interval expressed as standard deviations.
48 // At the minimum confidence level, there is a 25% chance that the true value of
49 // the estimate (average cycle time or allocation rate) is not more than
50 // MINIMUM_CONFIDENCE standard deviations away from our estimate. Similarly, the
51 // MAXIMUM_CONFIDENCE interval here means there is a one in a thousand chance
52 // that the true value of our estimate is outside the interval. These are used
53 // as bounds on the adjustments applied at the outcome of a GC cycle.
54 const double ShenandoahAdaptiveHeuristics::MINIMUM_CONFIDENCE = 0.319; // 25%
55 const double ShenandoahAdaptiveHeuristics::MAXIMUM_CONFIDENCE = 3.291; // 99.9%
56
57 ShenandoahAdaptiveHeuristics::ShenandoahAdaptiveHeuristics(ShenandoahGeneration* generation) :
58 ShenandoahHeuristics(generation),
59 _margin_of_error_sd(ShenandoahAdaptiveInitialConfidence),
60 _spike_threshold_sd(ShenandoahAdaptiveInitialSpikeThreshold),
61 _last_trigger(OTHER) { }
62
63 ShenandoahAdaptiveHeuristics::~ShenandoahAdaptiveHeuristics() {}
64
65 void ShenandoahAdaptiveHeuristics::choose_collection_set_from_regiondata(ShenandoahCollectionSet* cset,
66 RegionData* data, size_t size,
67 size_t actual_free) {
68 size_t garbage_threshold = ShenandoahHeapRegion::region_size_bytes() * ShenandoahGarbageThreshold / 100;
69
70 // The logic for cset selection in adaptive is as follows:
71 //
72 // 1. We cannot get cset larger than available free space. Otherwise we guarantee OOME
73 // during evacuation, and thus guarantee full GC. In practice, we also want to let
74 // application to allocate something. This is why we limit CSet to some fraction of
75 // available space. In non-overloaded heap, max_cset would contain all plausible candidates
76 // over garbage threshold.
77 //
78 // 2. We should not get cset too low so that free threshold would not be met right
79 // after the cycle. Otherwise we get back-to-back cycles for no reason if heap is
80 // too fragmented. In non-overloaded non-fragmented heap min_garbage would be around zero.
81 //
82 // Therefore, we start by sorting the regions by garbage. Then we unconditionally add the best candidates
83 // before we meet min_garbage. Then we add all candidates that fit with a garbage threshold before
84 // we hit max_cset. When max_cset is hit, we terminate the cset selection. Note that in this scheme,
85 // ShenandoahGarbageThreshold is the soft threshold which would be ignored until min_garbage is hit.
86
87 size_t capacity = _generation->soft_max_capacity();
88 size_t max_cset = (size_t)((1.0 * capacity / 100 * ShenandoahEvacReserve) / ShenandoahEvacWaste);
89 size_t free_target = (capacity / 100 * ShenandoahMinFreeThreshold) + max_cset;
90 size_t min_garbage = (free_target > actual_free ? (free_target - actual_free) : 0);
91
92 log_info(gc, ergo)("Adaptive CSet Selection. Target Free: " SIZE_FORMAT "%s, Actual Free: "
93 SIZE_FORMAT "%s, Max CSet: " SIZE_FORMAT "%s, Min Garbage: " SIZE_FORMAT "%s",
94 byte_size_in_proper_unit(free_target), proper_unit_for_byte_size(free_target),
95 byte_size_in_proper_unit(actual_free), proper_unit_for_byte_size(actual_free),
96 byte_size_in_proper_unit(max_cset), proper_unit_for_byte_size(max_cset),
97 byte_size_in_proper_unit(min_garbage), proper_unit_for_byte_size(min_garbage));
98
99 // Better select garbage-first regions
100 QuickSort::sort<RegionData>(data, (int)size, compare_by_garbage, false);
101
102 size_t cur_cset = 0;
103 size_t cur_garbage = 0;
104
105 for (size_t idx = 0; idx < size; idx++) {
106 ShenandoahHeapRegion* r = data[idx]._region;
107
108 size_t new_cset = cur_cset + r->get_live_data_bytes();
109 size_t new_garbage = cur_garbage + r->garbage();
110
111 if (new_cset > max_cset) {
112 break;
113 }
114
115 if ((new_garbage < min_garbage) || (r->garbage() > garbage_threshold)) {
116 cset->add_region(r);
117 cur_cset = new_cset;
118 cur_garbage = new_garbage;
119 }
120 }
121 }
122
123 void ShenandoahAdaptiveHeuristics::record_cycle_start() {
124 ShenandoahHeuristics::record_cycle_start();
125 _allocation_rate.allocation_counter_reset();
126 }
127
128 void ShenandoahAdaptiveHeuristics::record_success_concurrent() {
129 ShenandoahHeuristics::record_success_concurrent();
130
131 size_t available = ShenandoahHeap::heap()->free_set()->available();
132
133 _available.add(available);
134 double z_score = 0.0;
135 if (_available.sd() > 0) {
136 z_score = (available - _available.avg()) / _available.sd();
137 }
138
139 log_debug(gc, ergo)("Available: " SIZE_FORMAT " %sB, z-score=%.3f. Average available: %.1f %sB +/- %.1f %sB.",
140 byte_size_in_proper_unit(available), proper_unit_for_byte_size(available),
141 z_score,
142 byte_size_in_proper_unit(_available.avg()), proper_unit_for_byte_size(_available.avg()),
143 byte_size_in_proper_unit(_available.sd()), proper_unit_for_byte_size(_available.sd()));
144
145 // In the case when a concurrent GC cycle completes successfully but with an
146 // unusually small amount of available memory we will adjust our trigger
147 // parameters so that they are more likely to initiate a new cycle.
148 // Conversely, when a GC cycle results in an above average amount of available
149 // memory, we will adjust the trigger parameters to be less likely to initiate
150 // a GC cycle.
151 //
152 // The z-score we've computed is in no way statistically related to the
153 // trigger parameters, but it has the nice property that worse z-scores for
154 // available memory indicate making larger adjustments to the trigger
155 // parameters. It also results in fewer adjustments as the application
156 // stabilizes.
157 //
158 // In order to avoid making endless and likely unnecessary adjustments to the
159 // trigger parameters, the change in available memory (with respect to the
160 // average) at the end of a cycle must be beyond these threshold values.
161 if (z_score < LOWEST_EXPECTED_AVAILABLE_AT_END ||
162 z_score > HIGHEST_EXPECTED_AVAILABLE_AT_END) {
163 // The sign is flipped because a negative z-score indicates that the
164 // available memory at the end of the cycle is below average. Positive
165 // adjustments make the triggers more sensitive (i.e., more likely to fire).
166 // The z-score also gives us a measure of just how far below normal. This
167 // property allows us to adjust the trigger parameters proportionally.
168 //
169 // The `100` here is used to attenuate the size of our adjustments. This
170 // number was chosen empirically. It also means the adjustments at the end of
171 // a concurrent cycle are an order of magnitude smaller than the adjustments
172 // made for a degenerated or full GC cycle (which themselves were also
173 // chosen empirically).
174 adjust_last_trigger_parameters(z_score / -100);
175 }
176 }
177
178 void ShenandoahAdaptiveHeuristics::record_success_degenerated() {
179 ShenandoahHeuristics::record_success_degenerated();
180 // Adjust both trigger's parameters in the case of a degenerated GC because
181 // either of them should have triggered earlier to avoid this case.
182 adjust_margin_of_error(DEGENERATE_PENALTY_SD);
183 adjust_spike_threshold(DEGENERATE_PENALTY_SD);
184 }
185
186 void ShenandoahAdaptiveHeuristics::record_success_full() {
187 ShenandoahHeuristics::record_success_full();
188 // Adjust both trigger's parameters in the case of a full GC because
189 // either of them should have triggered earlier to avoid this case.
190 adjust_margin_of_error(FULL_PENALTY_SD);
191 adjust_spike_threshold(FULL_PENALTY_SD);
192 }
193
194 static double saturate(double value, double min, double max) {
195 return MAX2(MIN2(value, max), min);
196 }
197
198 bool ShenandoahAdaptiveHeuristics::should_start_gc() {
199 size_t max_capacity = _generation->max_capacity();
200 size_t capacity = _generation->soft_max_capacity();
201 size_t available = _generation->available();
202 size_t allocated = _generation->bytes_allocated_since_gc_start();
203
204 log_debug(gc)("should_start_gc (%s)? available: " SIZE_FORMAT ", soft_max_capacity: " SIZE_FORMAT
205 ", max_capacity: " SIZE_FORMAT ", allocated: " SIZE_FORMAT,
206 _generation->name(), available, capacity, max_capacity, allocated);
207
208 // Make sure the code below treats available without the soft tail.
209 size_t soft_tail = max_capacity - capacity;
210 available = (available > soft_tail) ? (available - soft_tail) : 0;
211
212 // Track allocation rate even if we decide to start a cycle for other reasons.
213 double rate = _allocation_rate.sample(allocated);
214 _last_trigger = OTHER;
215
216 size_t min_threshold = capacity / 100 * ShenandoahMinFreeThreshold;
217
218 log_debug(gc)(" available adjusted to: " SIZE_FORMAT ", min_threshold: " SIZE_FORMAT ", ShenandoahMinFreeThreshold: " SIZE_FORMAT,
219 available, min_threshold, ShenandoahMinFreeThreshold);
220
221 if (available < min_threshold) {
222 log_info(gc)("Trigger (%s): Free (" SIZE_FORMAT "%s) is below minimum threshold (" SIZE_FORMAT "%s)",
223 _generation->name(),
224 byte_size_in_proper_unit(available), proper_unit_for_byte_size(available),
225 byte_size_in_proper_unit(min_threshold), proper_unit_for_byte_size(min_threshold));
226 return true;
227 }
228
229 // Check if we need to learn a bit about the application
230 const size_t max_learn = ShenandoahLearningSteps;
231 if (_gc_times_learned < max_learn) {
232 size_t init_threshold = capacity / 100 * ShenandoahInitFreeThreshold;
233 if (available < init_threshold) {
234 log_info(gc)("Trigger (%s): Learning " SIZE_FORMAT " of " SIZE_FORMAT ". Free (" SIZE_FORMAT "%s) is below initial threshold (" SIZE_FORMAT "%s)",
235 _generation->name(), _gc_times_learned + 1, max_learn,
236 byte_size_in_proper_unit(available), proper_unit_for_byte_size(available),
237 byte_size_in_proper_unit(init_threshold), proper_unit_for_byte_size(init_threshold));
238 return true;
239 }
240 }
241
242 // Check if allocation headroom is still okay. This also factors in:
243 // 1. Some space to absorb allocation spikes
244 // 2. Accumulated penalties from Degenerated and Full GC
245 size_t allocation_headroom = available;
246
247 size_t spike_headroom = capacity / 100 * ShenandoahAllocSpikeFactor;
248 size_t penalties = capacity / 100 * _gc_time_penalties;
249
250 allocation_headroom -= MIN2(allocation_headroom, spike_headroom);
251 allocation_headroom -= MIN2(allocation_headroom, penalties);
252
253 double avg_cycle_time = _gc_time_history->davg() + (_margin_of_error_sd * _gc_time_history->dsd());
254 double avg_alloc_rate = _allocation_rate.upper_bound(_margin_of_error_sd);
255 log_debug(gc)("%s: average GC time: %.2f ms, allocation rate: %.0f %s/s",
256 _generation->name(), avg_cycle_time * 1000, byte_size_in_proper_unit(avg_alloc_rate), proper_unit_for_byte_size(avg_alloc_rate));
257
258 if (avg_cycle_time > allocation_headroom / avg_alloc_rate) {
259 log_info(gc)("Trigger (%s): Average GC time (%.2f ms) is above the time for average allocation rate (%.0f %sB/s) to deplete free headroom (" SIZE_FORMAT "%s) (margin of error = %.2f)",
260 _generation->name(), avg_cycle_time * 1000,
261 byte_size_in_proper_unit(avg_alloc_rate), proper_unit_for_byte_size(avg_alloc_rate),
262 byte_size_in_proper_unit(allocation_headroom), proper_unit_for_byte_size(allocation_headroom),
263 _margin_of_error_sd);
264
265 log_info(gc, ergo)("Free headroom: " SIZE_FORMAT "%s (free) - " SIZE_FORMAT "%s (spike) - " SIZE_FORMAT "%s (penalties) = " SIZE_FORMAT "%s",
266 byte_size_in_proper_unit(available), proper_unit_for_byte_size(available),
267 byte_size_in_proper_unit(spike_headroom), proper_unit_for_byte_size(spike_headroom),
268 byte_size_in_proper_unit(penalties), proper_unit_for_byte_size(penalties),
269 byte_size_in_proper_unit(allocation_headroom), proper_unit_for_byte_size(allocation_headroom));
270
271 _last_trigger = RATE;
272 return true;
273 }
274
275 bool is_spiking = _allocation_rate.is_spiking(rate, _spike_threshold_sd);
276 if (is_spiking && avg_cycle_time > allocation_headroom / rate) {
277 log_info(gc)("Trigger (%s): Average GC time (%.2f ms) is above the time for instantaneous allocation rate (%.0f %sB/s) to deplete free headroom (" SIZE_FORMAT "%s) (spike threshold = %.2f)",
278 _generation->name(), avg_cycle_time * 1000,
279 byte_size_in_proper_unit(rate), proper_unit_for_byte_size(rate),
280 byte_size_in_proper_unit(allocation_headroom), proper_unit_for_byte_size(allocation_headroom),
281 _spike_threshold_sd);
282 _last_trigger = SPIKE;
283 return true;
284 }
285
286 return ShenandoahHeuristics::should_start_gc();
287 }
288
289 void ShenandoahAdaptiveHeuristics::adjust_last_trigger_parameters(double amount) {
290 switch (_last_trigger) {
291 case RATE:
292 adjust_margin_of_error(amount);
293 break;
294 case SPIKE:
295 adjust_spike_threshold(amount);
296 break;
297 case OTHER:
298 // nothing to adjust here.
299 break;
300 default:
301 ShouldNotReachHere();
302 }
303 }
304
305 void ShenandoahAdaptiveHeuristics::adjust_margin_of_error(double amount) {
306 _margin_of_error_sd = saturate(_margin_of_error_sd + amount, MINIMUM_CONFIDENCE, MAXIMUM_CONFIDENCE);
307 log_debug(gc, ergo)("Margin of error now %.2f", _margin_of_error_sd);
308 }
309
310 void ShenandoahAdaptiveHeuristics::adjust_spike_threshold(double amount) {
311 _spike_threshold_sd = saturate(_spike_threshold_sd - amount, MINIMUM_CONFIDENCE, MAXIMUM_CONFIDENCE);
312 log_debug(gc, ergo)("Spike threshold now: %.2f", _spike_threshold_sd);
313 }
314
315 ShenandoahAllocationRate::ShenandoahAllocationRate() :
316 _last_sample_time(os::elapsedTime()),
317 _last_sample_value(0),
318 _interval_sec(1.0 / ShenandoahAdaptiveSampleFrequencyHz),
319 _rate(int(ShenandoahAdaptiveSampleSizeSeconds * ShenandoahAdaptiveSampleFrequencyHz), ShenandoahAdaptiveDecayFactor),
320 _rate_avg(int(ShenandoahAdaptiveSampleSizeSeconds * ShenandoahAdaptiveSampleFrequencyHz), ShenandoahAdaptiveDecayFactor) {
321 }
322
323 double ShenandoahAllocationRate::sample(size_t allocated) {
324 double now = os::elapsedTime();
325 double rate = 0.0;
326 if (now - _last_sample_time > _interval_sec) {
327 if (allocated >= _last_sample_value) {
328 rate = instantaneous_rate(now, allocated);
329 _rate.add(rate);
330 _rate_avg.add(_rate.avg());
331 }
332
333 _last_sample_time = now;
334 _last_sample_value = allocated;
335 }
336 return rate;
337 }
338
339 double ShenandoahAllocationRate::upper_bound(double sds) const {
340 // Here we are using the standard deviation of the computed running
341 // average, rather than the standard deviation of the samples that went
342 // into the moving average. This is a much more stable value and is tied
343 // to the actual statistic in use (moving average over samples of averages).
344 return _rate.davg() + (sds * _rate_avg.dsd());
345 }
346
347 void ShenandoahAllocationRate::allocation_counter_reset() {
348 _last_sample_time = os::elapsedTime();
349 _last_sample_value = 0;
350 }
351
352 bool ShenandoahAllocationRate::is_spiking(double rate, double threshold) const {
353 if (rate <= 0.0) {
354 return false;
355 }
356
357 double sd = _rate.sd();
358 if (sd > 0) {
359 // There is a small chance that that rate has already been sampled, but it
360 // seems not to matter in practice.
361 double z_score = (rate - _rate.avg()) / sd;
362 if (z_score > threshold) {
363 return true;
364 }
365 }
366 return false;
367 }
368
369 double ShenandoahAllocationRate::instantaneous_rate(size_t allocated) const {
370 return instantaneous_rate(os::elapsedTime(), allocated);
371 }
372
373 double ShenandoahAllocationRate::instantaneous_rate(double time, size_t allocated) const {
374 size_t last_value = _last_sample_value;
375 double last_time = _last_sample_time;
376 size_t allocation_delta = (allocated > last_value) ? (allocated - last_value) : 0;
377 double time_delta_sec = time - last_time;
378 return (time_delta_sec > 0) ? (allocation_delta / time_delta_sec) : 0;
379 }