--- /dev/null 2018-11-30 10:10:44.238550338 +0100 +++ new/src/hotspot/share/gc/shenandoah/heuristics/shenandoahAdaptiveHeuristics.cpp 2018-11-30 10:23:37.720881190 +0100 @@ -0,0 +1,204 @@ +/* + * Copyright (c) 2018, Red Hat, Inc. All rights reserved. + * + * This code is free software; you can redistribute it and/or modify it + * under the terms of the GNU General Public License version 2 only, as + * published by the Free Software Foundation. + * + * This code is distributed in the hope that it will be useful, but WITHOUT + * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or + * FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License + * version 2 for more details (a copy is included in the LICENSE file that + * accompanied this code). + * + * You should have received a copy of the GNU General Public License version + * 2 along with this work; if not, write to the Free Software Foundation, + * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA. + * + * Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA + * or visit www.oracle.com if you need additional information or have any + * questions. + * + */ + +#include "precompiled.hpp" + +#include "gc/shenandoah/heuristics/shenandoahAdaptiveHeuristics.hpp" +#include "gc/shenandoah/shenandoahCollectionSet.hpp" +#include "gc/shenandoah/shenandoahFreeSet.hpp" +#include "gc/shenandoah/shenandoahHeapRegion.hpp" +#include "logging/log.hpp" +#include "logging/logTag.hpp" +#include "utilities/quickSort.hpp" + +ShenandoahAdaptiveHeuristics::ShenandoahAdaptiveHeuristics() : + ShenandoahHeuristics(), + _cycle_gap_history(new TruncatedSeq(5)), + _conc_mark_duration_history(new TruncatedSeq(5)), + _conc_uprefs_duration_history(new TruncatedSeq(5)) { + + SHENANDOAH_ERGO_ENABLE_FLAG(ExplicitGCInvokesConcurrent); + SHENANDOAH_ERGO_ENABLE_FLAG(ShenandoahImplicitGCInvokesConcurrent); +} + +ShenandoahAdaptiveHeuristics::~ShenandoahAdaptiveHeuristics() {} + +void ShenandoahAdaptiveHeuristics::choose_collection_set_from_regiondata(ShenandoahCollectionSet* cset, + RegionData* data, size_t size, + size_t actual_free) { + size_t garbage_threshold = ShenandoahHeapRegion::region_size_bytes() * ShenandoahGarbageThreshold / 100; + + // The logic for cset selection in adaptive is as follows: + // + // 1. We cannot get cset larger than available free space. Otherwise we guarantee OOME + // during evacuation, and thus guarantee full GC. In practice, we also want to let + // application to allocate something. This is why we limit CSet to some fraction of + // available space. In non-overloaded heap, max_cset would contain all plausible candidates + // over garbage threshold. + // + // 2. We should not get cset too low so that free threshold would not be met right + // after the cycle. Otherwise we get back-to-back cycles for no reason if heap is + // too fragmented. In non-overloaded non-fragmented heap min_garbage would be around zero. + // + // Therefore, we start by sorting the regions by garbage. Then we unconditionally add the best candidates + // before we meet min_garbage. Then we add all candidates that fit with a garbage threshold before + // we hit max_cset. When max_cset is hit, we terminate the cset selection. Note that in this scheme, + // ShenandoahGarbageThreshold is the soft threshold which would be ignored until min_garbage is hit. + + size_t capacity = ShenandoahHeap::heap()->capacity(); + size_t free_target = ShenandoahMinFreeThreshold * capacity / 100; + size_t min_garbage = free_target > actual_free ? (free_target - actual_free) : 0; + size_t max_cset = (size_t)(1.0 * ShenandoahEvacReserve * capacity / 100 / ShenandoahEvacWaste); + + log_info(gc, ergo)("Adaptive CSet Selection. Target Free: " SIZE_FORMAT "M, Actual Free: " + SIZE_FORMAT "M, Max CSet: " SIZE_FORMAT "M, Min Garbage: " SIZE_FORMAT "M", + free_target / M, actual_free / M, max_cset / M, min_garbage / M); + + // Better select garbage-first regions + QuickSort::sort(data, (int)size, compare_by_garbage, false); + + size_t cur_cset = 0; + size_t cur_garbage = 0; + _bytes_in_cset = 0; + + for (size_t idx = 0; idx < size; idx++) { + ShenandoahHeapRegion* r = data[idx]._region; + + size_t new_cset = cur_cset + r->get_live_data_bytes(); + size_t new_garbage = cur_garbage + r->garbage(); + + if (new_cset > max_cset) { + break; + } + + if ((new_garbage < min_garbage) || (r->garbage() > garbage_threshold)) { + cset->add_region(r); + _bytes_in_cset += r->used(); + cur_cset = new_cset; + cur_garbage = new_garbage; + } + } +} + +void ShenandoahAdaptiveHeuristics::record_cycle_start() { + ShenandoahHeuristics::record_cycle_start(); + double last_cycle_gap = (_cycle_start - _last_cycle_end); + _cycle_gap_history->add(last_cycle_gap); +} + +void ShenandoahAdaptiveHeuristics::record_phase_time(ShenandoahPhaseTimings::Phase phase, double secs) { + if (phase == ShenandoahPhaseTimings::conc_mark) { + _conc_mark_duration_history->add(secs); + } else if (phase == ShenandoahPhaseTimings::conc_update_refs) { + _conc_uprefs_duration_history->add(secs); + } // Else ignore +} + +bool ShenandoahAdaptiveHeuristics::should_start_normal_gc() const { + ShenandoahHeap* heap = ShenandoahHeap::heap(); + size_t capacity = heap->capacity(); + size_t available = heap->free_set()->available(); + + // Check if we are falling below the worst limit, time to trigger the GC, regardless of + // anything else. + size_t min_threshold = ShenandoahMinFreeThreshold * heap->capacity() / 100; + if (available < min_threshold) { + log_info(gc)("Trigger: Free (" SIZE_FORMAT "M) is below minimum threshold (" SIZE_FORMAT "M)", + available / M, min_threshold / M); + return true; + } + + // Check if are need to learn a bit about the application + const size_t max_learn = ShenandoahLearningSteps; + if (_gc_times_learned < max_learn) { + size_t init_threshold = ShenandoahInitFreeThreshold * heap->capacity() / 100; + if (available < init_threshold) { + log_info(gc)("Trigger: Learning " SIZE_FORMAT " of " SIZE_FORMAT ". Free (" SIZE_FORMAT "M) is below initial threshold (" SIZE_FORMAT "M)", + _gc_times_learned + 1, max_learn, available / M, init_threshold / M); + return true; + } + } + + // Check if allocation headroom is still okay. This also factors in: + // 1. Some space to absorb allocation spikes + // 2. Accumulated penalties from Degenerated and Full GC + + size_t allocation_headroom = available; + + size_t spike_headroom = ShenandoahAllocSpikeFactor * capacity / 100; + size_t penalties = _gc_time_penalties * capacity / 100; + + allocation_headroom -= MIN2(allocation_headroom, spike_headroom); + allocation_headroom -= MIN2(allocation_headroom, penalties); + + // TODO: Allocation rate is way too averaged to be useful during state changes + + double average_gc = _gc_time_history->avg(); + double time_since_last = time_since_last_gc(); + double allocation_rate = heap->bytes_allocated_since_gc_start() / time_since_last; + + if (average_gc > allocation_headroom / allocation_rate) { + 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)", + average_gc * 1000, allocation_rate / M, allocation_headroom / M); + log_info(gc, ergo)("Free headroom: " SIZE_FORMAT "M (free) - " SIZE_FORMAT "M (spike) - " SIZE_FORMAT "M (penalties) = " SIZE_FORMAT "M", + available / M, spike_headroom / M, penalties / M, allocation_headroom / M); + return true; + } + + return ShenandoahHeuristics::should_start_normal_gc(); +} + +bool ShenandoahAdaptiveHeuristics::should_start_update_refs() { + if (! _update_refs_adaptive) { + return _update_refs_early; + } + + double cycle_gap_avg = _cycle_gap_history->avg(); + double conc_mark_avg = _conc_mark_duration_history->avg(); + double conc_uprefs_avg = _conc_uprefs_duration_history->avg(); + + if (_update_refs_early) { + double threshold = ShenandoahMergeUpdateRefsMinGap / 100.0; + if (conc_mark_avg + conc_uprefs_avg > cycle_gap_avg * threshold) { + _update_refs_early = false; + } + } else { + double threshold = ShenandoahMergeUpdateRefsMaxGap / 100.0; + if (conc_mark_avg + conc_uprefs_avg < cycle_gap_avg * threshold) { + _update_refs_early = true; + } + } + return _update_refs_early; +} + +const char* ShenandoahAdaptiveHeuristics::name() { + return "adaptive"; +} + +bool ShenandoahAdaptiveHeuristics::is_diagnostic() { + return false; +} + +bool ShenandoahAdaptiveHeuristics::is_experimental() { + return false; +}