/*

 * 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)) {}

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()->max_capacity();

  size_t free_target = capacity / 100 * ShenandoahMinFreeThreshold;

  size_t min_garbage = free_target > actual_free ? (free_target - actual_free) : 0;

  size_t max_cset    = (size_t)((1.0 * capacity / 100 * ShenandoahEvacReserve) / ShenandoahEvacWaste);

  // Better select garbage-first regions

  QuickSort::sort<RegionData>(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_gc() const {

  ShenandoahHeap* heap = ShenandoahHeap::heap();

  size_t capacity = heap->max_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 = capacity / 100 * ShenandoahMinFreeThreshold;

  if (available < min_threshold) {

    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 = capacity / 100 * ShenandoahInitFreeThreshold;

    if (available < init_threshold) {

      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 = capacity / 100 * ShenandoahAllocSpikeFactor;

  size_t penalties      = capacity / 100 * _gc_time_penalties;

  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) {

    return true;

  }

  return ShenandoahHeuristics::should_start_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;

}