Mercurial > hg > truffle
annotate src/share/vm/gc_implementation/shared/gcUtil.hpp @ 268:d6340ab4105b
6723228: NUMA allocator: assert(lgrp_id != -1, "No lgrp_id set")
6723229: NUMA allocator: assert(lgrp_num > 0, "There should be at least one locality group")
Summary: The fix takes care of the assertion triggered during TLAB resizing after reconfiguration. Also it now handles a defect in the topology graph, in which a single leaf node doesn't have memory.
Reviewed-by: jmasa
| author | iveresov |
|---|---|
| date | Thu, 17 Jul 2008 10:26:33 -0700 |
| parents | a61af66fc99e |
| children | 9ee9cf798b59 |
| rev | line source |
|---|---|
| 0 | 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 | |
|
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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 |
| 0 | 67 // Accessors |
| 68 float average() const { return _average; } | |
| 69 unsigned weight() const { return _weight; } | |
| 70 unsigned count() const { return _sample_count; } | |
| 71 float last_sample() const { return _last_sample; } | |
| 72 | |
| 73 // Update data with a new sample. | |
| 74 void sample(float new_sample); | |
| 75 | |
| 76 static inline float exp_avg(float avg, float sample, | |
| 77 unsigned int weight) { | |
| 78 assert(0 <= weight && weight <= 100, "weight must be a percent"); | |
| 79 return (100.0F - weight) * avg / 100.0F + weight * sample / 100.0F; | |
| 80 } | |
| 81 static inline size_t exp_avg(size_t avg, size_t sample, | |
| 82 unsigned int weight) { | |
| 83 // Convert to float and back to avoid integer overflow. | |
| 84 return (size_t)exp_avg((float)avg, (float)sample, weight); | |
| 85 } | |
| 86 }; | |
| 87 | |
| 88 | |
| 89 // A weighted average that includes a deviation from the average, | |
| 90 // some multiple of which is added to the average. | |
| 91 // | |
| 92 // This serves as our best estimate of an upper bound on a future | |
| 93 // unknown. | |
| 94 class AdaptivePaddedAverage : public AdaptiveWeightedAverage { | |
| 95 private: | |
| 96 float _padded_avg; // The last computed padded average | |
| 97 float _deviation; // Running deviation from the average | |
| 98 unsigned _padding; // A multiple which, added to the average, | |
| 99 // gives us an upper bound guess. | |
| 100 | |
| 101 protected: | |
| 102 void set_padded_average(float avg) { _padded_avg = avg; } | |
| 103 void set_deviation(float dev) { _deviation = dev; } | |
| 104 | |
| 105 public: | |
| 106 AdaptivePaddedAverage() : | |
| 107 AdaptiveWeightedAverage(0), | |
| 108 _padded_avg(0.0), _deviation(0.0), _padding(0) {} | |
| 109 | |
| 110 AdaptivePaddedAverage(unsigned weight, unsigned padding) : | |
| 111 AdaptiveWeightedAverage(weight), | |
| 112 _padded_avg(0.0), _deviation(0.0), _padding(padding) {} | |
| 113 | |
| 114 // Placement support | |
| 115 void* operator new(size_t ignored, void* p) { return p; } | |
| 116 // Allocator | |
| 117 void* operator new(size_t size) { return CHeapObj::operator new(size); } | |
| 118 | |
| 119 // Accessor | |
| 120 float padded_average() const { return _padded_avg; } | |
| 121 float deviation() const { return _deviation; } | |
| 122 unsigned padding() const { return _padding; } | |
| 123 | |
|
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124 void clear() { |
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125 AdaptiveWeightedAverage::clear(); |
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126 _padded_avg = 0; |
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127 _deviation = 0; |
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128 } |
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129 |
| 0 | 130 // Override |
| 131 void sample(float new_sample); | |
| 132 }; | |
| 133 | |
| 134 // A weighted average that includes a deviation from the average, | |
| 135 // some multiple of which is added to the average. | |
| 136 // | |
| 137 // This serves as our best estimate of an upper bound on a future | |
| 138 // unknown. | |
| 139 // A special sort of padded average: it doesn't update deviations | |
| 140 // if the sample is zero. The average is allowed to change. We're | |
| 141 // preventing the zero samples from drastically changing our padded | |
| 142 // average. | |
| 143 class AdaptivePaddedNoZeroDevAverage : public AdaptivePaddedAverage { | |
| 144 public: | |
| 145 AdaptivePaddedNoZeroDevAverage(unsigned weight, unsigned padding) : | |
| 146 AdaptivePaddedAverage(weight, padding) {} | |
| 147 // Override | |
| 148 void sample(float new_sample); | |
| 149 }; | |
| 150 // Use a least squares fit to a set of data to generate a linear | |
| 151 // equation. | |
| 152 // y = intercept + slope * x | |
| 153 | |
| 154 class LinearLeastSquareFit : public CHeapObj { | |
| 155 double _sum_x; // sum of all independent data points x | |
| 156 double _sum_x_squared; // sum of all independent data points x**2 | |
| 157 double _sum_y; // sum of all dependent data points y | |
| 158 double _sum_xy; // sum of all x * y. | |
| 159 double _intercept; // constant term | |
| 160 double _slope; // slope | |
| 161 // The weighted averages are not currently used but perhaps should | |
| 162 // be used to get decaying averages. | |
| 163 AdaptiveWeightedAverage _mean_x; // weighted mean of independent variable | |
| 164 AdaptiveWeightedAverage _mean_y; // weighted mean of dependent variable | |
| 165 | |
| 166 public: | |
| 167 LinearLeastSquareFit(unsigned weight); | |
| 168 void update(double x, double y); | |
| 169 double y(double x); | |
| 170 double slope() { return _slope; } | |
| 171 // Methods to decide if a change in the dependent variable will | |
| 172 // achive a desired goal. Note that these methods are not | |
| 173 // complementary and both are needed. | |
| 174 bool decrement_will_decrease(); | |
| 175 bool increment_will_decrease(); | |
| 176 }; | |
| 177 | |
| 178 class GCPauseTimer : StackObj { | |
| 179 elapsedTimer* _timer; | |
| 180 public: | |
| 181 GCPauseTimer(elapsedTimer* timer) { | |
| 182 _timer = timer; | |
| 183 _timer->stop(); | |
| 184 } | |
| 185 ~GCPauseTimer() { | |
| 186 _timer->start(); | |
| 187 } | |
| 188 }; |