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 }