1 /* 2 * Copyright (c) 2018, Red Hat, Inc. All rights reserved. 3 * 4 * This code is free software; you can redistribute it and/or modify it 5 * under the terms of the GNU General Public License version 2 only, as 6 * published by the Free Software Foundation. 7 * 8 * This code is distributed in the hope that it will be useful, but WITHOUT 9 * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or 10 * FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License 11 * version 2 for more details (a copy is included in the LICENSE file that 12 * accompanied this code). 13 * 14 * You should have received a copy of the GNU General Public License version 15 * 2 along with this work; if not, write to the Free Software Foundation, 16 * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA. 17 * 18 * Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA 19 * or visit www.oracle.com if you need additional information or have any 20 * questions. 21 * 22 */ 23 24 #include "precompiled.hpp" 25 26 #include "gc/shenandoah/heuristics/shenandoahAdaptiveHeuristics.hpp" 27 #include "gc/shenandoah/shenandoahCollectionSet.hpp" 28 #include "gc/shenandoah/shenandoahFreeSet.hpp" 29 #include "gc/shenandoah/shenandoahHeapRegion.hpp" 30 #include "logging/log.hpp" 31 #include "logging/logTag.hpp" 32 #include "utilities/quickSort.hpp" 33 34 ShenandoahAdaptiveHeuristics::ShenandoahAdaptiveHeuristics() : 35 ShenandoahHeuristics(), 36 _cycle_gap_history(new TruncatedSeq(5)), 37 _conc_mark_duration_history(new TruncatedSeq(5)), 38 _conc_uprefs_duration_history(new TruncatedSeq(5)) { 39 40 SHENANDOAH_ERGO_ENABLE_FLAG(ExplicitGCInvokesConcurrent); 41 SHENANDOAH_ERGO_ENABLE_FLAG(ShenandoahImplicitGCInvokesConcurrent); 42 43 // Final configuration checks 44 SHENANDOAH_CHECK_FLAG_SET(ShenandoahLoadRefBarrier); 45 SHENANDOAH_CHECK_FLAG_SET(ShenandoahSATBBarrier); 46 SHENANDOAH_CHECK_FLAG_SET(ShenandoahKeepAliveBarrier); 47 SHENANDOAH_CHECK_FLAG_SET(ShenandoahCASBarrier); 48 SHENANDOAH_CHECK_FLAG_SET(ShenandoahCloneBarrier); 49 } 50 51 ShenandoahAdaptiveHeuristics::~ShenandoahAdaptiveHeuristics() {} 52 53 void ShenandoahAdaptiveHeuristics::choose_collection_set_from_regiondata(ShenandoahCollectionSet* cset, 54 RegionData* data, size_t size, 55 size_t actual_free) { 56 size_t garbage_threshold = ShenandoahHeapRegion::region_size_bytes() * ShenandoahGarbageThreshold / 100; 57 58 // The logic for cset selection in adaptive is as follows: 59 // 60 // 1. We cannot get cset larger than available free space. Otherwise we guarantee OOME 61 // during evacuation, and thus guarantee full GC. In practice, we also want to let 62 // application to allocate something. This is why we limit CSet to some fraction of 63 // available space. In non-overloaded heap, max_cset would contain all plausible candidates 64 // over garbage threshold. 65 // 66 // 2. We should not get cset too low so that free threshold would not be met right 67 // after the cycle. Otherwise we get back-to-back cycles for no reason if heap is 68 // too fragmented. In non-overloaded non-fragmented heap min_garbage would be around zero. 69 // 70 // Therefore, we start by sorting the regions by garbage. Then we unconditionally add the best candidates 71 // before we meet min_garbage. Then we add all candidates that fit with a garbage threshold before 72 // we hit max_cset. When max_cset is hit, we terminate the cset selection. Note that in this scheme, 73 // ShenandoahGarbageThreshold is the soft threshold which would be ignored until min_garbage is hit. 74 75 size_t capacity = ShenandoahHeap::heap()->capacity(); 76 size_t free_target = ShenandoahMinFreeThreshold * capacity / 100; 77 size_t min_garbage = free_target > actual_free ? (free_target - actual_free) : 0; 78 size_t max_cset = (size_t)(1.0 * ShenandoahEvacReserve * capacity / 100 / ShenandoahEvacWaste); 79 80 log_info(gc, ergo)("Adaptive CSet Selection. Target Free: " SIZE_FORMAT "M, Actual Free: " 81 SIZE_FORMAT "M, Max CSet: " SIZE_FORMAT "M, Min Garbage: " SIZE_FORMAT "M", 82 free_target / M, actual_free / M, max_cset / M, min_garbage / M); 83 84 // Better select garbage-first regions 85 QuickSort::sort<RegionData>(data, (int)size, compare_by_garbage, false); 86 87 size_t cur_cset = 0; 88 size_t cur_garbage = 0; 89 _bytes_in_cset = 0; 90 91 for (size_t idx = 0; idx < size; idx++) { 92 ShenandoahHeapRegion* r = data[idx]._region; 93 94 size_t new_cset = cur_cset + r->get_live_data_bytes(); 95 size_t new_garbage = cur_garbage + r->garbage(); 96 97 if (new_cset > max_cset) { 98 break; 99 } 100 101 if ((new_garbage < min_garbage) || (r->garbage() > garbage_threshold)) { 102 cset->add_region(r); 103 _bytes_in_cset += r->used(); 104 cur_cset = new_cset; 105 cur_garbage = new_garbage; 106 } 107 } 108 } 109 110 void ShenandoahAdaptiveHeuristics::record_cycle_start() { 111 ShenandoahHeuristics::record_cycle_start(); 112 double last_cycle_gap = (_cycle_start - _last_cycle_end); 113 _cycle_gap_history->add(last_cycle_gap); 114 } 115 116 void ShenandoahAdaptiveHeuristics::record_phase_time(ShenandoahPhaseTimings::Phase phase, double secs) { 117 if (phase == ShenandoahPhaseTimings::conc_mark) { 118 _conc_mark_duration_history->add(secs); 119 } else if (phase == ShenandoahPhaseTimings::conc_update_refs) { 120 _conc_uprefs_duration_history->add(secs); 121 } // Else ignore 122 } 123 124 bool ShenandoahAdaptiveHeuristics::should_start_normal_gc() const { 125 ShenandoahHeap* heap = ShenandoahHeap::heap(); 126 size_t capacity = heap->capacity(); 127 size_t available = heap->free_set()->available(); 128 129 // Check if we are falling below the worst limit, time to trigger the GC, regardless of 130 // anything else. 131 size_t min_threshold = ShenandoahMinFreeThreshold * heap->capacity() / 100; 132 if (available < min_threshold) { 133 log_info(gc)("Trigger: Free (" SIZE_FORMAT "M) is below minimum threshold (" SIZE_FORMAT "M)", 134 available / M, min_threshold / M); 135 return true; 136 } 137 138 // Check if are need to learn a bit about the application 139 const size_t max_learn = ShenandoahLearningSteps; 140 if (_gc_times_learned < max_learn) { 141 size_t init_threshold = ShenandoahInitFreeThreshold * heap->capacity() / 100; 142 if (available < init_threshold) { 143 log_info(gc)("Trigger: Learning " SIZE_FORMAT " of " SIZE_FORMAT ". Free (" SIZE_FORMAT "M) is below initial threshold (" SIZE_FORMAT "M)", 144 _gc_times_learned + 1, max_learn, available / M, init_threshold / M); 145 return true; 146 } 147 } 148 149 // Check if allocation headroom is still okay. This also factors in: 150 // 1. Some space to absorb allocation spikes 151 // 2. Accumulated penalties from Degenerated and Full GC 152 153 size_t allocation_headroom = available; 154 155 size_t spike_headroom = ShenandoahAllocSpikeFactor * capacity / 100; 156 size_t penalties = _gc_time_penalties * capacity / 100; 157 158 allocation_headroom -= MIN2(allocation_headroom, spike_headroom); 159 allocation_headroom -= MIN2(allocation_headroom, penalties); 160 161 // TODO: Allocation rate is way too averaged to be useful during state changes 162 163 double average_gc = _gc_time_history->avg(); 164 double time_since_last = time_since_last_gc(); 165 double allocation_rate = heap->bytes_allocated_since_gc_start() / time_since_last; 166 167 if (average_gc > allocation_headroom / allocation_rate) { 168 log_info(gc)("Trigger: Average GC time (%.2f ms) is above the time for allocation rate (%.2f MB/s) to deplete free headroom (" SIZE_FORMAT "M)", 169 average_gc * 1000, allocation_rate / M, allocation_headroom / M); 170 log_info(gc, ergo)("Free headroom: " SIZE_FORMAT "M (free) - " SIZE_FORMAT "M (spike) - " SIZE_FORMAT "M (penalties) = " SIZE_FORMAT "M", 171 available / M, spike_headroom / M, penalties / M, allocation_headroom / M); 172 return true; 173 } 174 175 return ShenandoahHeuristics::should_start_normal_gc(); 176 } 177 178 bool ShenandoahAdaptiveHeuristics::should_start_update_refs() { 179 if (! _update_refs_adaptive) { 180 return _update_refs_early; 181 } 182 183 double cycle_gap_avg = _cycle_gap_history->avg(); 184 double conc_mark_avg = _conc_mark_duration_history->avg(); 185 double conc_uprefs_avg = _conc_uprefs_duration_history->avg(); 186 187 if (_update_refs_early) { 188 double threshold = ShenandoahMergeUpdateRefsMinGap / 100.0; 189 if (conc_mark_avg + conc_uprefs_avg > cycle_gap_avg * threshold) { 190 _update_refs_early = false; 191 } 192 } else { 193 double threshold = ShenandoahMergeUpdateRefsMaxGap / 100.0; 194 if (conc_mark_avg + conc_uprefs_avg < cycle_gap_avg * threshold) { 195 _update_refs_early = true; 196 } 197 } 198 return _update_refs_early; 199 } 200 201 const char* ShenandoahAdaptiveHeuristics::name() { 202 return "adaptive"; 203 } 204 205 bool ShenandoahAdaptiveHeuristics::is_diagnostic() { 206 return false; 207 } 208 209 bool ShenandoahAdaptiveHeuristics::is_experimental() { 210 return false; 211 }