Mercurial > hg > graal-compiler
view src/share/vm/gc_implementation/shared/allocationStats.hpp @ 266:2214b226b7f0
6724367: par compact could clear less young gen summary data
Reviewed-by: jmasa, apetrusenko
author | jcoomes |
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date | Fri, 11 Jul 2008 16:11:50 -0700 |
parents | 6432c3bb6240 |
children | d1605aabd0a1 |
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/* * Copyright 2001-2005 Sun Microsystems, Inc. All Rights Reserved. * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER. * * 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 Sun Microsystems, Inc., 4150 Network Circle, Santa Clara, * CA 95054 USA or visit www.sun.com if you need additional information or * have any questions. * */ class AllocationStats VALUE_OBJ_CLASS_SPEC { // A duration threshold (in ms) used to filter // possibly unreliable samples. static float _threshold; // We measure the demand between the end of the previous sweep and // beginning of this sweep: // Count(end_last_sweep) - Count(start_this_sweep) // + splitBirths(between) - splitDeaths(between) // The above number divided by the time since the start [END???] of the // previous sweep gives us a time rate of demand for blocks // of this size. We compute a padded average of this rate as // our current estimate for the time rate of demand for blocks // of this size. Similarly, we keep a padded average for the time // between sweeps. Our current estimate for demand for blocks of // this size is then simply computed as the product of these two // estimates. AdaptivePaddedAverage _demand_rate_estimate; ssize_t _desired; // Estimate computed as described above ssize_t _coalDesired; // desired +/- small-percent for tuning coalescing ssize_t _surplus; // count - (desired +/- small-percent), // used to tune splitting in best fit ssize_t _bfrSurp; // surplus at start of current sweep ssize_t _prevSweep; // count from end of previous sweep ssize_t _beforeSweep; // count from before current sweep ssize_t _coalBirths; // additional chunks from coalescing ssize_t _coalDeaths; // loss from coalescing ssize_t _splitBirths; // additional chunks from splitting ssize_t _splitDeaths; // loss from splitting size_t _returnedBytes; // number of bytes returned to list. public: void initialize() { AdaptivePaddedAverage* dummy = new (&_demand_rate_estimate) AdaptivePaddedAverage(CMS_FLSWeight, CMS_FLSPadding); _desired = 0; _coalDesired = 0; _surplus = 0; _bfrSurp = 0; _prevSweep = 0; _beforeSweep = 0; _coalBirths = 0; _coalDeaths = 0; _splitBirths = 0; _splitDeaths = 0; _returnedBytes = 0; } AllocationStats() { initialize(); } // The rate estimate is in blocks per second. void compute_desired(size_t count, float inter_sweep_current, float inter_sweep_estimate) { // If the latest inter-sweep time is below our granularity // of measurement, we may call in here with // inter_sweep_current == 0. However, even for suitably small // but non-zero inter-sweep durations, we may not trust the accuracy // of accumulated data, since it has not been "integrated" // (read "low-pass-filtered") long enough, and would be // vulnerable to noisy glitches. In such cases, we // ignore the current sample and use currently available // historical estimates. if (inter_sweep_current > _threshold) { ssize_t demand = prevSweep() - count + splitBirths() - splitDeaths(); float rate = ((float)demand)/inter_sweep_current; _demand_rate_estimate.sample(rate); _desired = (ssize_t)(_demand_rate_estimate.padded_average() *inter_sweep_estimate); } } ssize_t desired() const { return _desired; } void set_desired(ssize_t v) { _desired = v; } ssize_t coalDesired() const { return _coalDesired; } void set_coalDesired(ssize_t v) { _coalDesired = v; } ssize_t surplus() const { return _surplus; } void set_surplus(ssize_t v) { _surplus = v; } void increment_surplus() { _surplus++; } void decrement_surplus() { _surplus--; } ssize_t bfrSurp() const { return _bfrSurp; } void set_bfrSurp(ssize_t v) { _bfrSurp = v; } ssize_t prevSweep() const { return _prevSweep; } void set_prevSweep(ssize_t v) { _prevSweep = v; } ssize_t beforeSweep() const { return _beforeSweep; } void set_beforeSweep(ssize_t v) { _beforeSweep = v; } ssize_t coalBirths() const { return _coalBirths; } void set_coalBirths(ssize_t v) { _coalBirths = v; } void increment_coalBirths() { _coalBirths++; } ssize_t coalDeaths() const { return _coalDeaths; } void set_coalDeaths(ssize_t v) { _coalDeaths = v; } void increment_coalDeaths() { _coalDeaths++; } ssize_t splitBirths() const { return _splitBirths; } void set_splitBirths(ssize_t v) { _splitBirths = v; } void increment_splitBirths() { _splitBirths++; } ssize_t splitDeaths() const { return _splitDeaths; } void set_splitDeaths(ssize_t v) { _splitDeaths = v; } void increment_splitDeaths() { _splitDeaths++; } NOT_PRODUCT( size_t returnedBytes() const { return _returnedBytes; } void set_returnedBytes(size_t v) { _returnedBytes = v; } ) };