1 /* 2 * Copyright (c) 2018, 2019, Red Hat, Inc. All rights reserved. 3 * Copyright Amazon.com Inc. or its affiliates. All Rights Reserved. 4 * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER. 5 * 6 * This code is free software; you can redistribute it and/or modify it 7 * under the terms of the GNU General Public License version 2 only, as 8 * published by the Free Software Foundation. 9 * 10 * This code is distributed in the hope that it will be useful, but WITHOUT 11 * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or 12 * FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License 13 * version 2 for more details (a copy is included in the LICENSE file that 14 * accompanied this code). 15 * 16 * You should have received a copy of the GNU General Public License version 17 * 2 along with this work; if not, write to the Free Software Foundation, 18 * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA. 19 * 20 * Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA 21 * or visit www.oracle.com if you need additional information or have any 22 * questions. 23 * 24 */ 25 26 #include "precompiled.hpp" 27 28 #include "gc/shared/gcCause.hpp" 29 #include "gc/shenandoah/heuristics/shenandoahHeuristics.hpp" 30 #include "gc/shenandoah/heuristics/shenandoahSpaceInfo.hpp" 31 #include "gc/shenandoah/heuristics/shenandoahAdaptiveHeuristics.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 = _space_info->soft_max_capacity(); 95 size_t max_cset = (size_t)((1.0 * capacity / 100 * ShenandoahEvacReserve) / ShenandoahEvacWaste); 96 size_t free_target = (capacity * ShenandoahMinFreeThreshold) / 100 + 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: " SIZE_FORMAT "%s, Actual Free: " 100 SIZE_FORMAT "%s, Max Evacuation: " SIZE_FORMAT "%s, Min Garbage: " SIZE_FORMAT "%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)("%s Available: " SIZE_FORMAT " %sB, z-score=%.3f. Average available: %.1f %sB +/- %.1f %sB.", 146 _space_info->name(), 147 byte_size_in_proper_unit(available), proper_unit_for_byte_size(available), 148 z_score, 149 byte_size_in_proper_unit(available_avg), proper_unit_for_byte_size(available_avg), 150 byte_size_in_proper_unit(available_sd), proper_unit_for_byte_size(available_sd)); 151 } 152 153 _available.add(double(available)); 154 155 // In the case when a concurrent GC cycle completes successfully but with an 156 // unusually small amount of available memory we will adjust our trigger 157 // parameters so that they are more likely to initiate a new cycle. 158 // Conversely, when a GC cycle results in an above average amount of available 159 // memory, we will adjust the trigger parameters to be less likely to initiate 160 // a GC cycle. 161 // 162 // The z-score we've computed is in no way statistically related to the 163 // trigger parameters, but it has the nice property that worse z-scores for 164 // available memory indicate making larger adjustments to the trigger 165 // parameters. It also results in fewer adjustments as the application 166 // stabilizes. 167 // 168 // In order to avoid making endless and likely unnecessary adjustments to the 169 // trigger parameters, the change in available memory (with respect to the 170 // average) at the end of a cycle must be beyond these threshold values. 171 if (z_score < LOWEST_EXPECTED_AVAILABLE_AT_END || 172 z_score > HIGHEST_EXPECTED_AVAILABLE_AT_END) { 173 // The sign is flipped because a negative z-score indicates that the 174 // available memory at the end of the cycle is below average. Positive 175 // adjustments make the triggers more sensitive (i.e., more likely to fire). 176 // The z-score also gives us a measure of just how far below normal. This 177 // property allows us to adjust the trigger parameters proportionally. 178 // 179 // The `100` here is used to attenuate the size of our adjustments. This 180 // number was chosen empirically. It also means the adjustments at the end of 181 // a concurrent cycle are an order of magnitude smaller than the adjustments 182 // made for a degenerated or full GC cycle (which themselves were also 183 // chosen empirically). 184 adjust_last_trigger_parameters(z_score / -100); 185 } 186 } 187 188 void ShenandoahAdaptiveHeuristics::record_success_degenerated() { 189 ShenandoahHeuristics::record_success_degenerated(); 190 // Adjust both trigger's parameters in the case of a degenerated GC because 191 // either of them should have triggered earlier to avoid this case. 192 adjust_margin_of_error(DEGENERATE_PENALTY_SD); 193 adjust_spike_threshold(DEGENERATE_PENALTY_SD); 194 } 195 196 void ShenandoahAdaptiveHeuristics::record_success_full() { 197 ShenandoahHeuristics::record_success_full(); 198 // Adjust both trigger's parameters in the case of a full GC because 199 // either of them should have triggered earlier to avoid this case. 200 adjust_margin_of_error(FULL_PENALTY_SD); 201 adjust_spike_threshold(FULL_PENALTY_SD); 202 } 203 204 static double saturate(double value, double min, double max) { 205 return MAX2(MIN2(value, max), min); 206 } 207 208 // Rationale: 209 // The idea is that there is an average allocation rate and there are occasional abnormal bursts (or spikes) of 210 // allocations that exceed the average allocation rate. What do these spikes look like? 211 // 212 // 1. At certain phase changes, we may discard large amounts of data and replace it with large numbers of newly 213 // allocated objects. This "spike" looks more like a phase change. We were in steady state at M bytes/sec 214 // allocation rate and now we're in a "reinitialization phase" that looks like N bytes/sec. We need the "spike" 215 // accommodation to give us enough runway to recalibrate our "average allocation rate". 216 // 217 // 2. The typical workload changes. "Suddenly", our typical workload of N TPS increases to N+delta TPS. This means 218 // our average allocation rate needs to be adjusted. Once again, we need the "spike" accomodation to give us 219 // enough runway to recalibrate our "average allocation rate". 220 // 221 // 3. Though there is an "average" allocation rate, a given workload's demand for allocation may be very bursty. We 222 // allocate a bunch of LABs during the 5 ms that follow completion of a GC, then we perform no more allocations for 223 // the next 150 ms. It seems we want the "spike" to represent the maximum divergence from average within the 224 // period of time between consecutive evaluation of the should_start_gc() service. Here's the thinking: 225 // 226 // a) Between now and the next time I ask whether should_start_gc(), we might experience a spike representing 227 // the anticipated burst of allocations. If that would put us over budget, then we should start GC immediately. 228 // b) Between now and the anticipated depletion of allocation pool, there may be two or more bursts of allocations. 229 // If there are more than one of these bursts, we can "approximate" that these will be separated by spans of 230 // time with very little or no allocations so the "average" allocation rate should be a suitable approximation 231 // of how this will behave. 232 // 233 // For cases 1 and 2, we need to "quickly" recalibrate the average allocation rate whenever we detect a change 234 // in operation mode. We want some way to decide that the average rate has changed, while keeping average 235 // allocation rate computation independent. 236 bool ShenandoahAdaptiveHeuristics::should_start_gc() { 237 size_t capacity = _space_info->soft_max_capacity(); 238 size_t available = _space_info->soft_available(); 239 size_t allocated = _space_info->bytes_allocated_since_gc_start(); 240 241 log_debug(gc)("should_start_gc (%s)? available: " SIZE_FORMAT ", soft_max_capacity: " SIZE_FORMAT 242 ", allocated: " SIZE_FORMAT, 243 _space_info->name(), available, capacity, allocated); 244 245 // Track allocation rate even if we decide to start a cycle for other reasons. 246 double rate = _allocation_rate.sample(allocated); 247 _last_trigger = OTHER; 248 249 size_t min_threshold = min_free_threshold(); 250 if (available < min_threshold) { 251 log_info(gc)("Trigger (%s): Free (" SIZE_FORMAT "%s) is below minimum threshold (" SIZE_FORMAT "%s)", _space_info->name(), 252 byte_size_in_proper_unit(available), proper_unit_for_byte_size(available), 253 byte_size_in_proper_unit(min_threshold), proper_unit_for_byte_size(min_threshold)); 254 return true; 255 } 256 257 // Check if we need to learn a bit about the application 258 const size_t max_learn = ShenandoahLearningSteps; 259 if (_gc_times_learned < max_learn) { 260 size_t init_threshold = capacity / 100 * ShenandoahInitFreeThreshold; 261 if (available < init_threshold) { 262 log_info(gc)("Trigger (%s): Learning " SIZE_FORMAT " of " SIZE_FORMAT ". Free (" SIZE_FORMAT "%s) is below initial threshold (" SIZE_FORMAT "%s)", 263 _space_info->name(), _gc_times_learned + 1, max_learn, 264 byte_size_in_proper_unit(available), proper_unit_for_byte_size(available), 265 byte_size_in_proper_unit(init_threshold), proper_unit_for_byte_size(init_threshold)); 266 return true; 267 } 268 } 269 // Check if allocation headroom is still okay. This also factors in: 270 // 1. Some space to absorb allocation spikes (ShenandoahAllocSpikeFactor) 271 // 2. Accumulated penalties from Degenerated and Full GC 272 size_t allocation_headroom = available; 273 274 size_t spike_headroom = capacity / 100 * ShenandoahAllocSpikeFactor; 275 size_t penalties = capacity / 100 * _gc_time_penalties; 276 277 allocation_headroom -= MIN2(allocation_headroom, spike_headroom); 278 allocation_headroom -= MIN2(allocation_headroom, penalties); 279 280 double avg_cycle_time = _gc_cycle_time_history->davg() + (_margin_of_error_sd * _gc_cycle_time_history->dsd()); 281 double avg_alloc_rate = _allocation_rate.upper_bound(_margin_of_error_sd); 282 283 log_debug(gc)("%s: average GC time: %.2f ms, allocation rate: %.0f %s/s", 284 _space_info->name(), 285 avg_cycle_time * 1000, byte_size_in_proper_unit(avg_alloc_rate), proper_unit_for_byte_size(avg_alloc_rate)); 286 if (avg_cycle_time * avg_alloc_rate > allocation_headroom) { 287 log_info(gc)("Trigger (%s): Average GC time (%.2f ms) is above the time for average allocation rate (%.0f %sB/s)" 288 " to deplete free headroom (" SIZE_FORMAT "%s) (margin of error = %.2f)", 289 _space_info->name(), avg_cycle_time * 1000, 290 byte_size_in_proper_unit(avg_alloc_rate), proper_unit_for_byte_size(avg_alloc_rate), 291 byte_size_in_proper_unit(allocation_headroom), proper_unit_for_byte_size(allocation_headroom), 292 _margin_of_error_sd); 293 log_info(gc, ergo)("Free headroom: " SIZE_FORMAT "%s (free) - " SIZE_FORMAT "%s (spike) - " SIZE_FORMAT "%s (penalties) = " SIZE_FORMAT "%s", 294 byte_size_in_proper_unit(available), proper_unit_for_byte_size(available), 295 byte_size_in_proper_unit(spike_headroom), proper_unit_for_byte_size(spike_headroom), 296 byte_size_in_proper_unit(penalties), proper_unit_for_byte_size(penalties), 297 byte_size_in_proper_unit(allocation_headroom), proper_unit_for_byte_size(allocation_headroom)); 298 _last_trigger = RATE; 299 return true; 300 } 301 302 bool is_spiking = _allocation_rate.is_spiking(rate, _spike_threshold_sd); 303 if (is_spiking && avg_cycle_time > allocation_headroom / rate) { 304 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)", 305 _space_info->name(), avg_cycle_time * 1000, 306 byte_size_in_proper_unit(rate), proper_unit_for_byte_size(rate), 307 byte_size_in_proper_unit(allocation_headroom), proper_unit_for_byte_size(allocation_headroom), 308 _spike_threshold_sd); 309 _last_trigger = SPIKE; 310 return true; 311 } 312 313 return ShenandoahHeuristics::should_start_gc(); 314 } 315 316 void ShenandoahAdaptiveHeuristics::adjust_last_trigger_parameters(double amount) { 317 switch (_last_trigger) { 318 case RATE: 319 adjust_margin_of_error(amount); 320 break; 321 case SPIKE: 322 adjust_spike_threshold(amount); 323 break; 324 case OTHER: 325 // nothing to adjust here. 326 break; 327 default: 328 ShouldNotReachHere(); 329 } 330 } 331 332 void ShenandoahAdaptiveHeuristics::adjust_margin_of_error(double amount) { 333 _margin_of_error_sd = saturate(_margin_of_error_sd + amount, MINIMUM_CONFIDENCE, MAXIMUM_CONFIDENCE); 334 log_debug(gc, ergo)("Margin of error now %.2f", _margin_of_error_sd); 335 } 336 337 void ShenandoahAdaptiveHeuristics::adjust_spike_threshold(double amount) { 338 _spike_threshold_sd = saturate(_spike_threshold_sd - amount, MINIMUM_CONFIDENCE, MAXIMUM_CONFIDENCE); 339 log_debug(gc, ergo)("Spike threshold now: %.2f", _spike_threshold_sd); 340 } 341 342 size_t ShenandoahAdaptiveHeuristics::min_free_threshold() { 343 // Note that soft_max_capacity() / 100 * min_free_threshold is smaller than max_capacity() / 100 * min_free_threshold. 344 // We want to behave conservatively here, so use max_capacity(). By returning a larger value, we cause the GC to 345 // trigger when the remaining amount of free shrinks below the larger threshold. 346 return _space_info->max_capacity() / 100 * ShenandoahMinFreeThreshold; 347 } 348 349 ShenandoahAllocationRate::ShenandoahAllocationRate() : 350 _last_sample_time(os::elapsedTime()), 351 _last_sample_value(0), 352 _interval_sec(1.0 / ShenandoahAdaptiveSampleFrequencyHz), 353 _rate(int(ShenandoahAdaptiveSampleSizeSeconds * ShenandoahAdaptiveSampleFrequencyHz), ShenandoahAdaptiveDecayFactor), 354 _rate_avg(int(ShenandoahAdaptiveSampleSizeSeconds * ShenandoahAdaptiveSampleFrequencyHz), ShenandoahAdaptiveDecayFactor) { 355 } 356 357 double ShenandoahAllocationRate::sample(size_t allocated) { 358 double now = os::elapsedTime(); 359 double rate = 0.0; 360 if (now - _last_sample_time > _interval_sec) { 361 if (allocated >= _last_sample_value) { 362 rate = instantaneous_rate(now, allocated); 363 _rate.add(rate); 364 _rate_avg.add(_rate.avg()); 365 } 366 367 _last_sample_time = now; 368 _last_sample_value = allocated; 369 } 370 return rate; 371 } 372 373 double ShenandoahAllocationRate::upper_bound(double sds) const { 374 // Here we are using the standard deviation of the computed running 375 // average, rather than the standard deviation of the samples that went 376 // into the moving average. This is a much more stable value and is tied 377 // to the actual statistic in use (moving average over samples of averages). 378 return _rate.davg() + (sds * _rate_avg.dsd()); 379 } 380 381 void ShenandoahAllocationRate::allocation_counter_reset() { 382 _last_sample_time = os::elapsedTime(); 383 _last_sample_value = 0; 384 } 385 386 bool ShenandoahAllocationRate::is_spiking(double rate, double threshold) const { 387 if (rate <= 0.0) { 388 return false; 389 } 390 391 double sd = _rate.sd(); 392 if (sd > 0) { 393 // There is a small chance that that rate has already been sampled, but it 394 // seems not to matter in practice. 395 double z_score = (rate - _rate.avg()) / sd; 396 if (z_score > threshold) { 397 return true; 398 } 399 } 400 return false; 401 } 402 403 double ShenandoahAllocationRate::instantaneous_rate(double time, size_t allocated) const { 404 size_t last_value = _last_sample_value; 405 double last_time = _last_sample_time; 406 size_t allocation_delta = (allocated > last_value) ? (allocated - last_value) : 0; 407 double time_delta_sec = time - last_time; 408 return (time_delta_sec > 0) ? (allocation_delta / time_delta_sec) : 0; 409 }