Mercurial > jdk8-mips64-public > hotspot / file comparison
comparison: src/share/vm/gc_implementation/shared/gcUtil.hpp
src/share/vm/gc_implementation/shared/gcUtil.hpp
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| 1 /* | |
| 2 * Copyright 2002-2005 Sun Microsystems, Inc. All Rights Reserved. | |
| 3 * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER. | |
| 4 * | |
| 5 * This code is free software; you can redistribute it and/or modify it | |
| 6 * under the terms of the GNU General Public License version 2 only, as | |
| 7 * published by the Free Software Foundation. | |
| 8 * | |
| 9 * This code is distributed in the hope that it will be useful, but WITHOUT | |
| 10 * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or | |
| 11 * FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License | |
| 12 * version 2 for more details (a copy is included in the LICENSE file that | |
| 13 * accompanied this code). | |
| 14 * | |
| 15 * You should have received a copy of the GNU General Public License version | |
| 16 * 2 along with this work; if not, write to the Free Software Foundation, | |
| 17 * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA. | |
| 18 * | |
| 19 * Please contact Sun Microsystems, Inc., 4150 Network Circle, Santa Clara, | |
| 20 * CA 95054 USA or visit www.sun.com if you need additional information or | |
| 21 * have any questions. | |
| 22 * | |
| 23 */ | |
| 24 | |
| 25 // Catch-all file for utility classes | |
| 26 | |
| 27 // A weighted average maintains a running, weighted average | |
| 28 // of some float value (templates would be handy here if we | |
| 29 // need different types). | |
| 30 // | |
| 31 // The average is adaptive in that we smooth it for the | |
| 32 // initial samples; we don't use the weight until we have | |
| 33 // enough samples for it to be meaningful. | |
| 34 // | |
| 35 // This serves as our best estimate of a future unknown. | |
| 36 // | |
| 37 class AdaptiveWeightedAverage : public CHeapObj { | |
| 38 private: | |
| 39 float _average; // The last computed average | |
| 40 unsigned _sample_count; // How often we've sampled this average | |
| 41 unsigned _weight; // The weight used to smooth the averages | |
| 42 // A higher weight favors the most | |
| 43 // recent data. | |
| 44 | |
| 45 protected: | |
| 46 float _last_sample; // The last value sampled. | |
| 47 | |
| 48 void increment_count() { _sample_count++; } | |
| 49 void set_average(float avg) { _average = avg; } | |
| 50 | |
| 51 // Helper function, computes an adaptive weighted average | |
| 52 // given a sample and the last average | |
| 53 float compute_adaptive_average(float new_sample, float average); | |
| 54 | |
| 55 public: | |
| 56 // Input weight must be between 0 and 100 | |
| 57 AdaptiveWeightedAverage(unsigned weight) : | |
| 58 _average(0.0), _sample_count(0), _weight(weight), _last_sample(0.0) { | |
| 59 } | |
| 60 | |
| 61 // Accessors | |
| 62 float average() const { return _average; } | |
| 63 unsigned weight() const { return _weight; } | |
| 64 unsigned count() const { return _sample_count; } | |
| 65 float last_sample() const { return _last_sample; } | |
| 66 | |
| 67 // Update data with a new sample. | |
| 68 void sample(float new_sample); | |
| 69 | |
| 70 static inline float exp_avg(float avg, float sample, | |
| 71 unsigned int weight) { | |
| 72 assert(0 <= weight && weight <= 100, "weight must be a percent"); | |
| 73 return (100.0F - weight) * avg / 100.0F + weight * sample / 100.0F; | |
| 74 } | |
| 75 static inline size_t exp_avg(size_t avg, size_t sample, | |
| 76 unsigned int weight) { | |
| 77 // Convert to float and back to avoid integer overflow. | |
| 78 return (size_t)exp_avg((float)avg, (float)sample, weight); | |
| 79 } | |
| 80 }; | |
| 81 | |
| 82 | |
| 83 // A weighted average that includes a deviation from the average, | |
| 84 // some multiple of which is added to the average. | |
| 85 // | |
| 86 // This serves as our best estimate of an upper bound on a future | |
| 87 // unknown. | |
| 88 class AdaptivePaddedAverage : public AdaptiveWeightedAverage { | |
| 89 private: | |
| 90 float _padded_avg; // The last computed padded average | |
| 91 float _deviation; // Running deviation from the average | |
| 92 unsigned _padding; // A multiple which, added to the average, | |
| 93 // gives us an upper bound guess. | |
| 94 | |
| 95 protected: | |
| 96 void set_padded_average(float avg) { _padded_avg = avg; } | |
| 97 void set_deviation(float dev) { _deviation = dev; } | |
| 98 | |
| 99 public: | |
| 100 AdaptivePaddedAverage() : | |
| 101 AdaptiveWeightedAverage(0), | |
| 102 _padded_avg(0.0), _deviation(0.0), _padding(0) {} | |
| 103 | |
| 104 AdaptivePaddedAverage(unsigned weight, unsigned padding) : | |
| 105 AdaptiveWeightedAverage(weight), | |
| 106 _padded_avg(0.0), _deviation(0.0), _padding(padding) {} | |
| 107 | |
| 108 // Placement support | |
| 109 void* operator new(size_t ignored, void* p) { return p; } | |
| 110 // Allocator | |
| 111 void* operator new(size_t size) { return CHeapObj::operator new(size); } | |
| 112 | |
| 113 // Accessor | |
| 114 float padded_average() const { return _padded_avg; } | |
| 115 float deviation() const { return _deviation; } | |
| 116 unsigned padding() const { return _padding; } | |
| 117 | |
| 118 // Override | |
| 119 void sample(float new_sample); | |
| 120 }; | |
| 121 | |
| 122 // A weighted average that includes a deviation from the average, | |
| 123 // some multiple of which is added to the average. | |
| 124 // | |
| 125 // This serves as our best estimate of an upper bound on a future | |
| 126 // unknown. | |
| 127 // A special sort of padded average: it doesn't update deviations | |
| 128 // if the sample is zero. The average is allowed to change. We're | |
| 129 // preventing the zero samples from drastically changing our padded | |
| 130 // average. | |
| 131 class AdaptivePaddedNoZeroDevAverage : public AdaptivePaddedAverage { | |
| 132 public: | |
| 133 AdaptivePaddedNoZeroDevAverage(unsigned weight, unsigned padding) : | |
| 134 AdaptivePaddedAverage(weight, padding) {} | |
| 135 // Override | |
| 136 void sample(float new_sample); | |
| 137 }; | |
| 138 // Use a least squares fit to a set of data to generate a linear | |
| 139 // equation. | |
| 140 // y = intercept + slope * x | |
| 141 | |
| 142 class LinearLeastSquareFit : public CHeapObj { | |
| 143 double _sum_x; // sum of all independent data points x | |
| 144 double _sum_x_squared; // sum of all independent data points x**2 | |
| 145 double _sum_y; // sum of all dependent data points y | |
| 146 double _sum_xy; // sum of all x * y. | |
| 147 double _intercept; // constant term | |
| 148 double _slope; // slope | |
| 149 // The weighted averages are not currently used but perhaps should | |
| 150 // be used to get decaying averages. | |
| 151 AdaptiveWeightedAverage _mean_x; // weighted mean of independent variable | |
| 152 AdaptiveWeightedAverage _mean_y; // weighted mean of dependent variable | |
| 153 | |
| 154 public: | |
| 155 LinearLeastSquareFit(unsigned weight); | |
| 156 void update(double x, double y); | |
| 157 double y(double x); | |
| 158 double slope() { return _slope; } | |
| 159 // Methods to decide if a change in the dependent variable will | |
| 160 // achive a desired goal. Note that these methods are not | |
| 161 // complementary and both are needed. | |
| 162 bool decrement_will_decrease(); | |
| 163 bool increment_will_decrease(); | |
| 164 }; | |
| 165 | |
| 166 class GCPauseTimer : StackObj { | |
| 167 elapsedTimer* _timer; | |
| 168 public: | |
| 169 GCPauseTimer(elapsedTimer* timer) { | |
| 170 _timer = timer; | |
| 171 _timer->stop(); | |
| 172 } | |
| 173 ~GCPauseTimer() { | |
| 174 _timer->start(); | |
| 175 } | |
| 176 }; |
