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annotate src/share/vm/gc_implementation/shared/gcUtil.hpp @ 6294:b0fc02623974
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author | Doug Simon <doug.simon@oracle.com> |
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date | Tue, 28 Aug 2012 13:55:11 +0200 |
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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 // | |
6197 | 46 class AdaptiveWeightedAverage : public CHeapObj<mtGC> { |
0 | 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. | |
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53 bool _is_old; // Has enough historical data |
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54 |
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55 const static unsigned OLD_THRESHOLD = 100; |
0 | 56 |
57 protected: | |
58 float _last_sample; // The last value sampled. | |
59 | |
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60 void increment_count() { |
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61 _sample_count++; |
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62 if (!_is_old && _sample_count > OLD_THRESHOLD) { |
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63 _is_old = true; |
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64 } |
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65 } |
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66 |
0 | 67 void set_average(float avg) { _average = avg; } |
68 | |
69 // Helper function, computes an adaptive weighted average | |
70 // given a sample and the last average | |
71 float compute_adaptive_average(float new_sample, float average); | |
72 | |
73 public: | |
74 // Input weight must be between 0 and 100 | |
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75 AdaptiveWeightedAverage(unsigned weight, float avg = 0.0) : |
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76 _average(avg), _sample_count(0), _weight(weight), _last_sample(0.0), |
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77 _is_old(false) { |
0 | 78 } |
79 | |
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80 void clear() { |
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81 _average = 0; |
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82 _sample_count = 0; |
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83 _last_sample = 0; |
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84 _is_old = false; |
268
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85 } |
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86 |
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87 // Useful for modifying static structures after startup. |
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88 void modify(size_t avg, unsigned wt, bool force = false) { |
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89 assert(force, "Are you sure you want to call this?"); |
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90 _average = (float)avg; |
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91 _weight = wt; |
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92 } |
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93 |
0 | 94 // Accessors |
95 float average() const { return _average; } | |
96 unsigned weight() const { return _weight; } | |
97 unsigned count() const { return _sample_count; } | |
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98 float last_sample() const { return _last_sample; } |
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99 bool is_old() const { return _is_old; } |
0 | 100 |
101 // Update data with a new sample. | |
102 void sample(float new_sample); | |
103 | |
104 static inline float exp_avg(float avg, float sample, | |
105 unsigned int weight) { | |
106 assert(0 <= weight && weight <= 100, "weight must be a percent"); | |
107 return (100.0F - weight) * avg / 100.0F + weight * sample / 100.0F; | |
108 } | |
109 static inline size_t exp_avg(size_t avg, size_t sample, | |
110 unsigned int weight) { | |
111 // Convert to float and back to avoid integer overflow. | |
112 return (size_t)exp_avg((float)avg, (float)sample, weight); | |
113 } | |
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114 |
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115 // Printing |
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116 void print_on(outputStream* st) const; |
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117 void print() const; |
0 | 118 }; |
119 | |
120 | |
121 // A weighted average that includes a deviation from the average, | |
122 // some multiple of which is added to the average. | |
123 // | |
124 // This serves as our best estimate of an upper bound on a future | |
125 // unknown. | |
126 class AdaptivePaddedAverage : public AdaptiveWeightedAverage { | |
127 private: | |
128 float _padded_avg; // The last computed padded average | |
129 float _deviation; // Running deviation from the average | |
130 unsigned _padding; // A multiple which, added to the average, | |
131 // gives us an upper bound guess. | |
132 | |
133 protected: | |
134 void set_padded_average(float avg) { _padded_avg = avg; } | |
135 void set_deviation(float dev) { _deviation = dev; } | |
136 | |
137 public: | |
138 AdaptivePaddedAverage() : | |
139 AdaptiveWeightedAverage(0), | |
140 _padded_avg(0.0), _deviation(0.0), _padding(0) {} | |
141 | |
142 AdaptivePaddedAverage(unsigned weight, unsigned padding) : | |
143 AdaptiveWeightedAverage(weight), | |
144 _padded_avg(0.0), _deviation(0.0), _padding(padding) {} | |
145 | |
146 // Placement support | |
147 void* operator new(size_t ignored, void* p) { return p; } | |
148 // Allocator | |
6197 | 149 void* operator new(size_t size) { return CHeapObj<mtGC>::operator new(size); } |
0 | 150 |
151 // Accessor | |
152 float padded_average() const { return _padded_avg; } | |
153 float deviation() const { return _deviation; } | |
154 unsigned padding() const { return _padding; } | |
155 | |
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156 void clear() { |
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157 AdaptiveWeightedAverage::clear(); |
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158 _padded_avg = 0; |
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159 _deviation = 0; |
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160 } |
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161 |
0 | 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 }; |
169 | |
170 // A weighted average that includes a deviation from the average, | |
171 // some multiple of which is added to the average. | |
172 // | |
173 // This serves as our best estimate of an upper bound on a future | |
174 // unknown. | |
175 // A special sort of padded average: it doesn't update deviations | |
176 // if the sample is zero. The average is allowed to change. We're | |
177 // preventing the zero samples from drastically changing our padded | |
178 // average. | |
179 class AdaptivePaddedNoZeroDevAverage : public AdaptivePaddedAverage { | |
180 public: | |
181 AdaptivePaddedNoZeroDevAverage(unsigned weight, unsigned padding) : | |
182 AdaptivePaddedAverage(weight, padding) {} | |
183 // Override | |
184 void sample(float new_sample); | |
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185 |
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186 // Printing |
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187 void print_on(outputStream* st) const; |
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188 void print() const; |
0 | 189 }; |
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190 |
0 | 191 // Use a least squares fit to a set of data to generate a linear |
192 // equation. | |
193 // y = intercept + slope * x | |
194 | |
6197 | 195 class LinearLeastSquareFit : public CHeapObj<mtGC> { |
0 | 196 double _sum_x; // sum of all independent data points x |
197 double _sum_x_squared; // sum of all independent data points x**2 | |
198 double _sum_y; // sum of all dependent data points y | |
199 double _sum_xy; // sum of all x * y. | |
200 double _intercept; // constant term | |
201 double _slope; // slope | |
202 // The weighted averages are not currently used but perhaps should | |
203 // be used to get decaying averages. | |
204 AdaptiveWeightedAverage _mean_x; // weighted mean of independent variable | |
205 AdaptiveWeightedAverage _mean_y; // weighted mean of dependent variable | |
206 | |
207 public: | |
208 LinearLeastSquareFit(unsigned weight); | |
209 void update(double x, double y); | |
210 double y(double x); | |
211 double slope() { return _slope; } | |
212 // Methods to decide if a change in the dependent variable will | |
213 // achive a desired goal. Note that these methods are not | |
214 // complementary and both are needed. | |
215 bool decrement_will_decrease(); | |
216 bool increment_will_decrease(); | |
217 }; | |
218 | |
219 class GCPauseTimer : StackObj { | |
220 elapsedTimer* _timer; | |
221 public: | |
222 GCPauseTimer(elapsedTimer* timer) { | |
223 _timer = timer; | |
224 _timer->stop(); | |
225 } | |
226 ~GCPauseTimer() { | |
227 _timer->start(); | |
228 } | |
229 }; | |
1972 | 230 |
231 #endif // SHARE_VM_GC_IMPLEMENTATION_SHARED_GCUTIL_HPP |