Mercurial > hg > graal-compiler
annotate src/share/vm/gc_implementation/shared/gcUtil.hpp @ 6008:b632e80fc9dc
4988100: oop_verify_old_oop appears to be dead
Summary: removed oop_verify_old_oop and allow_dirty. Also reviewed by: alexlamsl@gmail.com
Reviewed-by: jmasa, jwilhelm
| author | brutisso |
|---|---|
| date | Mon, 16 Apr 2012 08:57:18 +0200 |
| parents | f95d63e2154a |
| children | 78a1b285cda8 |
| rev | line source |
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| 0 | 1 /* |
| 1972 | 2 * Copyright (c) 2002, 2010, Oracle and/or its affiliates. All rights reserved. |
| 0 | 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 * | |
|
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19 * Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA |
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20 * or visit www.oracle.com if you need additional information or have any |
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21 * questions. |
| 0 | 22 * |
| 23 */ | |
| 24 | |
| 1972 | 25 #ifndef SHARE_VM_GC_IMPLEMENTATION_SHARED_GCUTIL_HPP |
| 26 #define SHARE_VM_GC_IMPLEMENTATION_SHARED_GCUTIL_HPP | |
| 27 | |
| 28 #include "memory/allocation.hpp" | |
| 29 #include "runtime/timer.hpp" | |
| 30 #include "utilities/debug.hpp" | |
| 31 #include "utilities/globalDefinitions.hpp" | |
| 32 #include "utilities/ostream.hpp" | |
| 33 | |
| 0 | 34 // Catch-all file for utility classes |
| 35 | |
| 36 // A weighted average maintains a running, weighted average | |
| 37 // of some float value (templates would be handy here if we | |
| 38 // need different types). | |
| 39 // | |
| 40 // The average is adaptive in that we smooth it for the | |
| 41 // initial samples; we don't use the weight until we have | |
| 42 // enough samples for it to be meaningful. | |
| 43 // | |
| 44 // This serves as our best estimate of a future unknown. | |
| 45 // | |
| 46 class AdaptiveWeightedAverage : public CHeapObj { | |
| 47 private: | |
| 48 float _average; // The last computed average | |
| 49 unsigned _sample_count; // How often we've sampled this average | |
| 50 unsigned _weight; // The weight used to smooth the averages | |
| 51 // A higher weight favors the most | |
| 52 // recent data. | |
| 53 | |
| 54 protected: | |
| 55 float _last_sample; // The last value sampled. | |
| 56 | |
| 57 void increment_count() { _sample_count++; } | |
| 58 void set_average(float avg) { _average = avg; } | |
| 59 | |
| 60 // Helper function, computes an adaptive weighted average | |
| 61 // given a sample and the last average | |
| 62 float compute_adaptive_average(float new_sample, float average); | |
| 63 | |
| 64 public: | |
| 65 // Input weight must be between 0 and 100 | |
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66 AdaptiveWeightedAverage(unsigned weight, float avg = 0.0) : |
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67 _average(avg), _sample_count(0), _weight(weight), _last_sample(0.0) { |
| 0 | 68 } |
| 69 | |
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70 void clear() { |
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71 _average = 0; |
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72 _sample_count = 0; |
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73 _last_sample = 0; |
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74 } |
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75 |
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76 // Useful for modifying static structures after startup. |
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77 void modify(size_t avg, unsigned wt, bool force = false) { |
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78 assert(force, "Are you sure you want to call this?"); |
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79 _average = (float)avg; |
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80 _weight = wt; |
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81 } |
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82 |
| 0 | 83 // Accessors |
| 84 float average() const { return _average; } | |
| 85 unsigned weight() const { return _weight; } | |
| 86 unsigned count() const { return _sample_count; } | |
| 87 float last_sample() const { return _last_sample; } | |
| 88 | |
| 89 // Update data with a new sample. | |
| 90 void sample(float new_sample); | |
| 91 | |
| 92 static inline float exp_avg(float avg, float sample, | |
| 93 unsigned int weight) { | |
| 94 assert(0 <= weight && weight <= 100, "weight must be a percent"); | |
| 95 return (100.0F - weight) * avg / 100.0F + weight * sample / 100.0F; | |
| 96 } | |
| 97 static inline size_t exp_avg(size_t avg, size_t sample, | |
| 98 unsigned int weight) { | |
| 99 // Convert to float and back to avoid integer overflow. | |
| 100 return (size_t)exp_avg((float)avg, (float)sample, weight); | |
| 101 } | |
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102 |
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103 // Printing |
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104 void print_on(outputStream* st) const; |
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105 void print() const; |
| 0 | 106 }; |
| 107 | |
| 108 | |
| 109 // A weighted average that includes a deviation from the average, | |
| 110 // some multiple of which is added to the average. | |
| 111 // | |
| 112 // This serves as our best estimate of an upper bound on a future | |
| 113 // unknown. | |
| 114 class AdaptivePaddedAverage : public AdaptiveWeightedAverage { | |
| 115 private: | |
| 116 float _padded_avg; // The last computed padded average | |
| 117 float _deviation; // Running deviation from the average | |
| 118 unsigned _padding; // A multiple which, added to the average, | |
| 119 // gives us an upper bound guess. | |
| 120 | |
| 121 protected: | |
| 122 void set_padded_average(float avg) { _padded_avg = avg; } | |
| 123 void set_deviation(float dev) { _deviation = dev; } | |
| 124 | |
| 125 public: | |
| 126 AdaptivePaddedAverage() : | |
| 127 AdaptiveWeightedAverage(0), | |
| 128 _padded_avg(0.0), _deviation(0.0), _padding(0) {} | |
| 129 | |
| 130 AdaptivePaddedAverage(unsigned weight, unsigned padding) : | |
| 131 AdaptiveWeightedAverage(weight), | |
| 132 _padded_avg(0.0), _deviation(0.0), _padding(padding) {} | |
| 133 | |
| 134 // Placement support | |
| 135 void* operator new(size_t ignored, void* p) { return p; } | |
| 136 // Allocator | |
| 137 void* operator new(size_t size) { return CHeapObj::operator new(size); } | |
| 138 | |
| 139 // Accessor | |
| 140 float padded_average() const { return _padded_avg; } | |
| 141 float deviation() const { return _deviation; } | |
| 142 unsigned padding() const { return _padding; } | |
| 143 | |
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144 void clear() { |
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145 AdaptiveWeightedAverage::clear(); |
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146 _padded_avg = 0; |
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147 _deviation = 0; |
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148 } |
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149 |
| 0 | 150 // Override |
| 151 void sample(float new_sample); | |
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152 |
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153 // Printing |
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154 void print_on(outputStream* st) const; |
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155 void print() const; |
| 0 | 156 }; |
| 157 | |
| 158 // A weighted average that includes a deviation from the average, | |
| 159 // some multiple of which is added to the average. | |
| 160 // | |
| 161 // This serves as our best estimate of an upper bound on a future | |
| 162 // unknown. | |
| 163 // A special sort of padded average: it doesn't update deviations | |
| 164 // if the sample is zero. The average is allowed to change. We're | |
| 165 // preventing the zero samples from drastically changing our padded | |
| 166 // average. | |
| 167 class AdaptivePaddedNoZeroDevAverage : public AdaptivePaddedAverage { | |
| 168 public: | |
| 169 AdaptivePaddedNoZeroDevAverage(unsigned weight, unsigned padding) : | |
| 170 AdaptivePaddedAverage(weight, padding) {} | |
| 171 // Override | |
| 172 void sample(float new_sample); | |
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173 |
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174 // Printing |
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175 void print_on(outputStream* st) const; |
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176 void print() const; |
| 0 | 177 }; |
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178 |
| 0 | 179 // Use a least squares fit to a set of data to generate a linear |
| 180 // equation. | |
| 181 // y = intercept + slope * x | |
| 182 | |
| 183 class LinearLeastSquareFit : public CHeapObj { | |
| 184 double _sum_x; // sum of all independent data points x | |
| 185 double _sum_x_squared; // sum of all independent data points x**2 | |
| 186 double _sum_y; // sum of all dependent data points y | |
| 187 double _sum_xy; // sum of all x * y. | |
| 188 double _intercept; // constant term | |
| 189 double _slope; // slope | |
| 190 // The weighted averages are not currently used but perhaps should | |
| 191 // be used to get decaying averages. | |
| 192 AdaptiveWeightedAverage _mean_x; // weighted mean of independent variable | |
| 193 AdaptiveWeightedAverage _mean_y; // weighted mean of dependent variable | |
| 194 | |
| 195 public: | |
| 196 LinearLeastSquareFit(unsigned weight); | |
| 197 void update(double x, double y); | |
| 198 double y(double x); | |
| 199 double slope() { return _slope; } | |
| 200 // Methods to decide if a change in the dependent variable will | |
| 201 // achive a desired goal. Note that these methods are not | |
| 202 // complementary and both are needed. | |
| 203 bool decrement_will_decrease(); | |
| 204 bool increment_will_decrease(); | |
| 205 }; | |
| 206 | |
| 207 class GCPauseTimer : StackObj { | |
| 208 elapsedTimer* _timer; | |
| 209 public: | |
| 210 GCPauseTimer(elapsedTimer* timer) { | |
| 211 _timer = timer; | |
| 212 _timer->stop(); | |
| 213 } | |
| 214 ~GCPauseTimer() { | |
| 215 _timer->start(); | |
| 216 } | |
| 217 }; | |
| 1972 | 218 |
| 219 #endif // SHARE_VM_GC_IMPLEMENTATION_SHARED_GCUTIL_HPP |