Mercurial > hg > truffle
annotate src/share/vm/gc_implementation/shared/gcUtil.hpp @ 1656:4e5661ba9d98
6944166: G1: explicit GCs are not always handled correctly
Summary: G1 was not handling explicit GCs correctly in many ways. It does now. See the CR for the list of improvements contained in this changeset.
Reviewed-by: iveresov, ysr, johnc
| author | tonyp |
|---|---|
| date | Mon, 28 Jun 2010 14:13:17 -0400 |
| parents | c18cbe5936b8 |
| children | f95d63e2154a |
| rev | line source |
|---|---|
| 0 | 1 /* |
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2 * Copyright (c) 2002, 2008, 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 | |
| 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 | |
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57 AdaptiveWeightedAverage(unsigned weight, float avg = 0.0) : |
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58 _average(avg), _sample_count(0), _weight(weight), _last_sample(0.0) { |
| 0 | 59 } |
| 60 | |
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61 void clear() { |
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62 _average = 0; |
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63 _sample_count = 0; |
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64 _last_sample = 0; |
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65 } |
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66 |
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67 // Useful for modifying static structures after startup. |
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68 void modify(size_t avg, unsigned wt, bool force = false) { |
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69 assert(force, "Are you sure you want to call this?"); |
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70 _average = (float)avg; |
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71 _weight = wt; |
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72 } |
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73 |
| 0 | 74 // Accessors |
| 75 float average() const { return _average; } | |
| 76 unsigned weight() const { return _weight; } | |
| 77 unsigned count() const { return _sample_count; } | |
| 78 float last_sample() const { return _last_sample; } | |
| 79 | |
| 80 // Update data with a new sample. | |
| 81 void sample(float new_sample); | |
| 82 | |
| 83 static inline float exp_avg(float avg, float sample, | |
| 84 unsigned int weight) { | |
| 85 assert(0 <= weight && weight <= 100, "weight must be a percent"); | |
| 86 return (100.0F - weight) * avg / 100.0F + weight * sample / 100.0F; | |
| 87 } | |
| 88 static inline size_t exp_avg(size_t avg, size_t sample, | |
| 89 unsigned int weight) { | |
| 90 // Convert to float and back to avoid integer overflow. | |
| 91 return (size_t)exp_avg((float)avg, (float)sample, weight); | |
| 92 } | |
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93 |
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94 // Printing |
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95 void print_on(outputStream* st) const; |
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96 void print() const; |
| 0 | 97 }; |
| 98 | |
| 99 | |
| 100 // A weighted average that includes a deviation from the average, | |
| 101 // some multiple of which is added to the average. | |
| 102 // | |
| 103 // This serves as our best estimate of an upper bound on a future | |
| 104 // unknown. | |
| 105 class AdaptivePaddedAverage : public AdaptiveWeightedAverage { | |
| 106 private: | |
| 107 float _padded_avg; // The last computed padded average | |
| 108 float _deviation; // Running deviation from the average | |
| 109 unsigned _padding; // A multiple which, added to the average, | |
| 110 // gives us an upper bound guess. | |
| 111 | |
| 112 protected: | |
| 113 void set_padded_average(float avg) { _padded_avg = avg; } | |
| 114 void set_deviation(float dev) { _deviation = dev; } | |
| 115 | |
| 116 public: | |
| 117 AdaptivePaddedAverage() : | |
| 118 AdaptiveWeightedAverage(0), | |
| 119 _padded_avg(0.0), _deviation(0.0), _padding(0) {} | |
| 120 | |
| 121 AdaptivePaddedAverage(unsigned weight, unsigned padding) : | |
| 122 AdaptiveWeightedAverage(weight), | |
| 123 _padded_avg(0.0), _deviation(0.0), _padding(padding) {} | |
| 124 | |
| 125 // Placement support | |
| 126 void* operator new(size_t ignored, void* p) { return p; } | |
| 127 // Allocator | |
| 128 void* operator new(size_t size) { return CHeapObj::operator new(size); } | |
| 129 | |
| 130 // Accessor | |
| 131 float padded_average() const { return _padded_avg; } | |
| 132 float deviation() const { return _deviation; } | |
| 133 unsigned padding() const { return _padding; } | |
| 134 | |
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135 void clear() { |
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136 AdaptiveWeightedAverage::clear(); |
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137 _padded_avg = 0; |
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138 _deviation = 0; |
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139 } |
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140 |
| 0 | 141 // Override |
| 142 void sample(float new_sample); | |
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143 |
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144 // Printing |
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145 void print_on(outputStream* st) const; |
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146 void print() const; |
| 0 | 147 }; |
| 148 | |
| 149 // A weighted average that includes a deviation from the average, | |
| 150 // some multiple of which is added to the average. | |
| 151 // | |
| 152 // This serves as our best estimate of an upper bound on a future | |
| 153 // unknown. | |
| 154 // A special sort of padded average: it doesn't update deviations | |
| 155 // if the sample is zero. The average is allowed to change. We're | |
| 156 // preventing the zero samples from drastically changing our padded | |
| 157 // average. | |
| 158 class AdaptivePaddedNoZeroDevAverage : public AdaptivePaddedAverage { | |
| 159 public: | |
| 160 AdaptivePaddedNoZeroDevAverage(unsigned weight, unsigned padding) : | |
| 161 AdaptivePaddedAverage(weight, padding) {} | |
| 162 // Override | |
| 163 void sample(float new_sample); | |
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164 |
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165 // Printing |
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166 void print_on(outputStream* st) const; |
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167 void print() const; |
| 0 | 168 }; |
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169 |
| 0 | 170 // Use a least squares fit to a set of data to generate a linear |
| 171 // equation. | |
| 172 // y = intercept + slope * x | |
| 173 | |
| 174 class LinearLeastSquareFit : public CHeapObj { | |
| 175 double _sum_x; // sum of all independent data points x | |
| 176 double _sum_x_squared; // sum of all independent data points x**2 | |
| 177 double _sum_y; // sum of all dependent data points y | |
| 178 double _sum_xy; // sum of all x * y. | |
| 179 double _intercept; // constant term | |
| 180 double _slope; // slope | |
| 181 // The weighted averages are not currently used but perhaps should | |
| 182 // be used to get decaying averages. | |
| 183 AdaptiveWeightedAverage _mean_x; // weighted mean of independent variable | |
| 184 AdaptiveWeightedAverage _mean_y; // weighted mean of dependent variable | |
| 185 | |
| 186 public: | |
| 187 LinearLeastSquareFit(unsigned weight); | |
| 188 void update(double x, double y); | |
| 189 double y(double x); | |
| 190 double slope() { return _slope; } | |
| 191 // Methods to decide if a change in the dependent variable will | |
| 192 // achive a desired goal. Note that these methods are not | |
| 193 // complementary and both are needed. | |
| 194 bool decrement_will_decrease(); | |
| 195 bool increment_will_decrease(); | |
| 196 }; | |
| 197 | |
| 198 class GCPauseTimer : StackObj { | |
| 199 elapsedTimer* _timer; | |
| 200 public: | |
| 201 GCPauseTimer(elapsedTimer* timer) { | |
| 202 _timer = timer; | |
| 203 _timer->stop(); | |
| 204 } | |
| 205 ~GCPauseTimer() { | |
| 206 _timer->start(); | |
| 207 } | |
| 208 }; |