Tue, 13 Apr 2010 13:52:10 -0700
6858496: Clear all SoftReferences before an out-of-memory due to GC overhead limit.
Summary: Ensure a full GC that clears SoftReferences before throwing an out-of-memory
Reviewed-by: ysr, jcoomes
1 /*
2 * Copyright 2002-2008 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 */
25 // Catch-all file for utility classes
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.
45 protected:
46 float _last_sample; // The last value sampled.
48 void increment_count() { _sample_count++; }
49 void set_average(float avg) { _average = avg; }
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);
55 public:
56 // Input weight must be between 0 and 100
57 AdaptiveWeightedAverage(unsigned weight, float avg = 0.0) :
58 _average(avg), _sample_count(0), _weight(weight), _last_sample(0.0) {
59 }
61 void clear() {
62 _average = 0;
63 _sample_count = 0;
64 _last_sample = 0;
65 }
67 // Useful for modifying static structures after startup.
68 void modify(size_t avg, unsigned wt, bool force = false) {
69 assert(force, "Are you sure you want to call this?");
70 _average = (float)avg;
71 _weight = wt;
72 }
74 // Accessors
75 float average() const { return _average; }
76 unsigned weight() const { return _weight; }
77 unsigned count() const { return _sample_count; }
78 float last_sample() const { return _last_sample; }
80 // Update data with a new sample.
81 void sample(float new_sample);
83 static inline float exp_avg(float avg, float sample,
84 unsigned int weight) {
85 assert(0 <= weight && weight <= 100, "weight must be a percent");
86 return (100.0F - weight) * avg / 100.0F + weight * sample / 100.0F;
87 }
88 static inline size_t exp_avg(size_t avg, size_t sample,
89 unsigned int weight) {
90 // Convert to float and back to avoid integer overflow.
91 return (size_t)exp_avg((float)avg, (float)sample, weight);
92 }
94 // Printing
95 void print_on(outputStream* st) const;
96 void print() const;
97 };
100 // A weighted average that includes a deviation from the average,
101 // some multiple of which is added to the average.
102 //
103 // This serves as our best estimate of an upper bound on a future
104 // unknown.
105 class AdaptivePaddedAverage : public AdaptiveWeightedAverage {
106 private:
107 float _padded_avg; // The last computed padded average
108 float _deviation; // Running deviation from the average
109 unsigned _padding; // A multiple which, added to the average,
110 // gives us an upper bound guess.
112 protected:
113 void set_padded_average(float avg) { _padded_avg = avg; }
114 void set_deviation(float dev) { _deviation = dev; }
116 public:
117 AdaptivePaddedAverage() :
118 AdaptiveWeightedAverage(0),
119 _padded_avg(0.0), _deviation(0.0), _padding(0) {}
121 AdaptivePaddedAverage(unsigned weight, unsigned padding) :
122 AdaptiveWeightedAverage(weight),
123 _padded_avg(0.0), _deviation(0.0), _padding(padding) {}
125 // Placement support
126 void* operator new(size_t ignored, void* p) { return p; }
127 // Allocator
128 void* operator new(size_t size) { return CHeapObj::operator new(size); }
130 // Accessor
131 float padded_average() const { return _padded_avg; }
132 float deviation() const { return _deviation; }
133 unsigned padding() const { return _padding; }
135 void clear() {
136 AdaptiveWeightedAverage::clear();
137 _padded_avg = 0;
138 _deviation = 0;
139 }
141 // Override
142 void sample(float new_sample);
144 // Printing
145 void print_on(outputStream* st) const;
146 void print() const;
147 };
149 // A weighted average that includes a deviation from the average,
150 // some multiple of which is added to the average.
151 //
152 // This serves as our best estimate of an upper bound on a future
153 // unknown.
154 // A special sort of padded average: it doesn't update deviations
155 // if the sample is zero. The average is allowed to change. We're
156 // preventing the zero samples from drastically changing our padded
157 // average.
158 class AdaptivePaddedNoZeroDevAverage : public AdaptivePaddedAverage {
159 public:
160 AdaptivePaddedNoZeroDevAverage(unsigned weight, unsigned padding) :
161 AdaptivePaddedAverage(weight, padding) {}
162 // Override
163 void sample(float new_sample);
165 // Printing
166 void print_on(outputStream* st) const;
167 void print() const;
168 };
170 // Use a least squares fit to a set of data to generate a linear
171 // equation.
172 // y = intercept + slope * x
174 class LinearLeastSquareFit : public CHeapObj {
175 double _sum_x; // sum of all independent data points x
176 double _sum_x_squared; // sum of all independent data points x**2
177 double _sum_y; // sum of all dependent data points y
178 double _sum_xy; // sum of all x * y.
179 double _intercept; // constant term
180 double _slope; // slope
181 // The weighted averages are not currently used but perhaps should
182 // be used to get decaying averages.
183 AdaptiveWeightedAverage _mean_x; // weighted mean of independent variable
184 AdaptiveWeightedAverage _mean_y; // weighted mean of dependent variable
186 public:
187 LinearLeastSquareFit(unsigned weight);
188 void update(double x, double y);
189 double y(double x);
190 double slope() { return _slope; }
191 // Methods to decide if a change in the dependent variable will
192 // achive a desired goal. Note that these methods are not
193 // complementary and both are needed.
194 bool decrement_will_decrease();
195 bool increment_will_decrease();
196 };
198 class GCPauseTimer : StackObj {
199 elapsedTimer* _timer;
200 public:
201 GCPauseTimer(elapsedTimer* timer) {
202 _timer = timer;
203 _timer->stop();
204 }
205 ~GCPauseTimer() {
206 _timer->start();
207 }
208 };