1 /*
2 * Copyright (c) 2002, 2010, Oracle and/or its affiliates. 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 Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA
20 * or visit www.oracle.com if you need additional information or have any
21 * questions.
22 *
23 */
24
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
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
66 AdaptiveWeightedAverage(unsigned weight, float avg = 0.0) :
67 _average(avg), _sample_count(0), _weight(weight), _last_sample(0.0) {
68 }
69
70 void clear() {
71 _average = 0;
72 _sample_count = 0;
73 _last_sample = 0;
74 }
75
76 // Useful for modifying static structures after startup.
77 void modify(size_t avg, unsigned wt, bool force = false) {
78 assert(force, "Are you sure you want to call this?");
79 _average = (float)avg;
80 _weight = wt;
81 }
82
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 }
102
103 // Printing
104 void print_on(outputStream* st) const;
105 void print() const;
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
144 void clear() {
145 AdaptiveWeightedAverage::clear();
146 _padded_avg = 0;
147 _deviation = 0;
148 }
149
150 // Override
151 void sample(float new_sample);
152
153 // Printing
154 void print_on(outputStream* st) const;
155 void print() const;
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);
173
174 // Printing
175 void print_on(outputStream* st) const;
176 void print() const;
177 };
178
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 };
218
219 #endif // SHARE_VM_GC_IMPLEMENTATION_SHARED_GCUTIL_HPP