Mercurial > hg > graal-jvmci-8
view src/share/vm/gc_implementation/shared/gcUtil.hpp @ 807:d44bdab1c03d
6843694: G1: assert(index < _vs.committed_size(),"bad index"), g1BlockOffsetTable.inline.hpp:55
Summary: For heaps larger than 32Gb, the number of heap regions overflows the data type used to hold the region index in the SparsePRT structure. Changed the region indexes, card indexes, and RSet hash table buckets to ints and added some size overflow guarantees.
Reviewed-by: ysr, tonyp
author | johnc |
---|---|
date | Thu, 11 Jun 2009 17:19:33 -0700 |
parents | 9ee9cf798b59 |
children | e018e6884bd8 |
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/* * Copyright 2002-2008 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. * */ // Catch-all file for utility classes // A weighted average maintains a running, weighted average // of some float value (templates would be handy here if we // need different types). // // The average is adaptive in that we smooth it for the // initial samples; we don't use the weight until we have // enough samples for it to be meaningful. // // This serves as our best estimate of a future unknown. // class AdaptiveWeightedAverage : public CHeapObj { private: float _average; // The last computed average unsigned _sample_count; // How often we've sampled this average unsigned _weight; // The weight used to smooth the averages // A higher weight favors the most // recent data. protected: float _last_sample; // The last value sampled. void increment_count() { _sample_count++; } void set_average(float avg) { _average = avg; } // Helper function, computes an adaptive weighted average // given a sample and the last average float compute_adaptive_average(float new_sample, float average); public: // Input weight must be between 0 and 100 AdaptiveWeightedAverage(unsigned weight) : _average(0.0), _sample_count(0), _weight(weight), _last_sample(0.0) { } void clear() { _average = 0; _sample_count = 0; _last_sample = 0; } // Accessors float average() const { return _average; } unsigned weight() const { return _weight; } unsigned count() const { return _sample_count; } float last_sample() const { return _last_sample; } // Update data with a new sample. void sample(float new_sample); static inline float exp_avg(float avg, float sample, unsigned int weight) { assert(0 <= weight && weight <= 100, "weight must be a percent"); return (100.0F - weight) * avg / 100.0F + weight * sample / 100.0F; } static inline size_t exp_avg(size_t avg, size_t sample, unsigned int weight) { // Convert to float and back to avoid integer overflow. return (size_t)exp_avg((float)avg, (float)sample, weight); } }; // A weighted average that includes a deviation from the average, // some multiple of which is added to the average. // // This serves as our best estimate of an upper bound on a future // unknown. class AdaptivePaddedAverage : public AdaptiveWeightedAverage { private: float _padded_avg; // The last computed padded average float _deviation; // Running deviation from the average unsigned _padding; // A multiple which, added to the average, // gives us an upper bound guess. protected: void set_padded_average(float avg) { _padded_avg = avg; } void set_deviation(float dev) { _deviation = dev; } public: AdaptivePaddedAverage() : AdaptiveWeightedAverage(0), _padded_avg(0.0), _deviation(0.0), _padding(0) {} AdaptivePaddedAverage(unsigned weight, unsigned padding) : AdaptiveWeightedAverage(weight), _padded_avg(0.0), _deviation(0.0), _padding(padding) {} // Placement support void* operator new(size_t ignored, void* p) { return p; } // Allocator void* operator new(size_t size) { return CHeapObj::operator new(size); } // Accessor float padded_average() const { return _padded_avg; } float deviation() const { return _deviation; } unsigned padding() const { return _padding; } void clear() { AdaptiveWeightedAverage::clear(); _padded_avg = 0; _deviation = 0; } // Override void sample(float new_sample); }; // A weighted average that includes a deviation from the average, // some multiple of which is added to the average. // // This serves as our best estimate of an upper bound on a future // unknown. // A special sort of padded average: it doesn't update deviations // if the sample is zero. The average is allowed to change. We're // preventing the zero samples from drastically changing our padded // average. class AdaptivePaddedNoZeroDevAverage : public AdaptivePaddedAverage { public: AdaptivePaddedNoZeroDevAverage(unsigned weight, unsigned padding) : AdaptivePaddedAverage(weight, padding) {} // Override void sample(float new_sample); }; // Use a least squares fit to a set of data to generate a linear // equation. // y = intercept + slope * x class LinearLeastSquareFit : public CHeapObj { double _sum_x; // sum of all independent data points x double _sum_x_squared; // sum of all independent data points x**2 double _sum_y; // sum of all dependent data points y double _sum_xy; // sum of all x * y. double _intercept; // constant term double _slope; // slope // The weighted averages are not currently used but perhaps should // be used to get decaying averages. AdaptiveWeightedAverage _mean_x; // weighted mean of independent variable AdaptiveWeightedAverage _mean_y; // weighted mean of dependent variable public: LinearLeastSquareFit(unsigned weight); void update(double x, double y); double y(double x); double slope() { return _slope; } // Methods to decide if a change in the dependent variable will // achive a desired goal. Note that these methods are not // complementary and both are needed. bool decrement_will_decrease(); bool increment_will_decrease(); }; class GCPauseTimer : StackObj { elapsedTimer* _timer; public: GCPauseTimer(elapsedTimer* timer) { _timer = timer; _timer->stop(); } ~GCPauseTimer() { _timer->start(); } };