1/*
2 * Copyright (c) 2002, 2019, Oracle and/or its affiliates. All rights reserved.
3 * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
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5 * This code is free software; you can redistribute it and/or modify it
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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.
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23 */
24
25#ifndef SHARE_GC_SHARED_GCUTIL_HPP
26#define SHARE_GC_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//
46class AdaptiveWeightedAverage : public CHeapObj<mtGC> {
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 bool _is_old; // Has enough historical data
54
55 const static unsigned OLD_THRESHOLD = 100;
56
57 protected:
58 float _last_sample; // The last value sampled.
59
60 void increment_count() {
61 _sample_count++;
62 if (!_is_old && _sample_count > OLD_THRESHOLD) {
63 _is_old = true;
64 }
65 }
66
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
75 AdaptiveWeightedAverage(unsigned weight, float avg = 0.0) :
76 _average(avg), _sample_count(0), _weight(weight),
77 _is_old(false), _last_sample(0.0) {
78 }
79
80 void clear() {
81 _average = 0;
82 _sample_count = 0;
83 _last_sample = 0;
84 _is_old = false;
85 }
86
87 // Useful for modifying static structures after startup.
88 void modify(size_t avg, unsigned wt, bool force = false) {
89 assert(force, "Are you sure you want to call this?");
90 _average = (float)avg;
91 _weight = wt;
92 }
93
94 // Accessors
95 float average() const { return _average; }
96 unsigned weight() const { return _weight; }
97 unsigned count() const { return _sample_count; }
98 float last_sample() const { return _last_sample; }
99 bool is_old() const { return _is_old; }
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(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 }
114
115 // Printing
116 void print_on(outputStream* st) const;
117 void print() const;
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.
126class 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) throw() { return p; }
148 // Allocator
149 void* operator new(size_t size) throw();
150
151 // Accessor
152 float padded_average() const { return _padded_avg; }
153 float deviation() const { return _deviation; }
154 unsigned padding() const { return _padding; }
155
156 void clear() {
157 AdaptiveWeightedAverage::clear();
158 _padded_avg = 0;
159 _deviation = 0;
160 }
161
162 // Override
163 void sample(float new_sample);
164
165 // Printing
166 void print_on(outputStream* st) const;
167 void print() const;
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.
179class AdaptivePaddedNoZeroDevAverage : public AdaptivePaddedAverage {
180public:
181 AdaptivePaddedNoZeroDevAverage(unsigned weight, unsigned padding) :
182 AdaptivePaddedAverage(weight, padding) {}
183 // Override
184 void sample(float new_sample);
185
186 // Printing
187 void print_on(outputStream* st) const;
188 void print() const;
189};
190
191// Use a least squares fit to a set of data to generate a linear
192// equation.
193// y = intercept + slope * x
194
195class LinearLeastSquareFit : public CHeapObj<mtGC> {
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 // achieve 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#endif // SHARE_GC_SHARED_GCUTIL_HPP
220