Mercurial > hg > graal-jvmci-8
comparison src/share/vm/gc_implementation/shared/gcUtil.hpp @ 0:a61af66fc99e jdk7-b24
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author | duke |
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date | Sat, 01 Dec 2007 00:00:00 +0000 |
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children | d6340ab4105b |
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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 }; |