## OpenJDK / amber / amber

### view src/hotspot/share/gc/shared/gcUtil.hpp @ 50590:4fa726f796f5

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8202781: Fix typo in DiscoveredListIterator::complete_enqeue
Reviewed-by: kbarrett

author | tschatzl |
---|---|

date | Tue, 08 May 2018 16:49:20 +0200 |

parents | 71c04702a3d5 |

children | c25572739e7c |

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/* * Copyright (c) 2002, 2015, Oracle and/or its affiliates. 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 Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA * or visit www.oracle.com if you need additional information or have any * questions. * */ #ifndef SHARE_VM_GC_SHARED_GCUTIL_HPP #define SHARE_VM_GC_SHARED_GCUTIL_HPP #include "memory/allocation.hpp" #include "runtime/timer.hpp" #include "utilities/debug.hpp" #include "utilities/globalDefinitions.hpp" #include "utilities/ostream.hpp" // 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<mtGC> { 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. bool _is_old; // Has enough historical data const static unsigned OLD_THRESHOLD = 100; protected: float _last_sample; // The last value sampled. void increment_count() { _sample_count++; if (!_is_old && _sample_count > OLD_THRESHOLD) { _is_old = true; } } 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, float avg = 0.0) : _average(avg), _sample_count(0), _weight(weight), _last_sample(0.0), _is_old(false) { } void clear() { _average = 0; _sample_count = 0; _last_sample = 0; _is_old = false; } // Useful for modifying static structures after startup. void modify(size_t avg, unsigned wt, bool force = false) { assert(force, "Are you sure you want to call this?"); _average = (float)avg; _weight = wt; } // Accessors float average() const { return _average; } unsigned weight() const { return _weight; } unsigned count() const { return _sample_count; } float last_sample() const { return _last_sample; } bool is_old() const { return _is_old; } // Update data with a new sample. void sample(float new_sample); static inline float exp_avg(float avg, float sample, unsigned int weight) { assert(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); } // Printing void print_on(outputStream* st) const; void print() const; }; // 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) throw() { return p; } // Allocator void* operator new(size_t size) throw(); // 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); // Printing void print_on(outputStream* st) const; void print() const; }; // 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); // Printing void print_on(outputStream* st) const; void print() const; }; // Use a least squares fit to a set of data to generate a linear // equation. // y = intercept + slope * x class LinearLeastSquareFit : public CHeapObj<mtGC> { 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 // achieve a desired goal. Note that these methods are not // complementary and both are needed. bool decrement_will_decrease(); bool increment_will_decrease(); }; #endif // SHARE_VM_GC_SHARED_GCUTIL_HPP