1.1 --- /dev/null Thu Jan 01 00:00:00 1970 +0000
1.2 +++ b/src/share/vm/gc_implementation/shared/gcUtil.hpp Sat Dec 01 00:00:00 2007 +0000
1.3 @@ -0,0 +1,176 @@
1.4 +/*
1.5 + * Copyright 2002-2005 Sun Microsystems, Inc. All Rights Reserved.
1.6 + * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
1.7 + *
1.8 + * This code is free software; you can redistribute it and/or modify it
1.9 + * under the terms of the GNU General Public License version 2 only, as
1.10 + * published by the Free Software Foundation.
1.11 + *
1.12 + * This code is distributed in the hope that it will be useful, but WITHOUT
1.13 + * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
1.14 + * FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
1.15 + * version 2 for more details (a copy is included in the LICENSE file that
1.16 + * accompanied this code).
1.17 + *
1.18 + * You should have received a copy of the GNU General Public License version
1.19 + * 2 along with this work; if not, write to the Free Software Foundation,
1.20 + * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
1.21 + *
1.22 + * Please contact Sun Microsystems, Inc., 4150 Network Circle, Santa Clara,
1.23 + * CA 95054 USA or visit www.sun.com if you need additional information or
1.24 + * have any questions.
1.25 + *
1.26 + */
1.27 +
1.28 +// Catch-all file for utility classes
1.29 +
1.30 +// A weighted average maintains a running, weighted average
1.31 +// of some float value (templates would be handy here if we
1.32 +// need different types).
1.33 +//
1.34 +// The average is adaptive in that we smooth it for the
1.35 +// initial samples; we don't use the weight until we have
1.36 +// enough samples for it to be meaningful.
1.37 +//
1.38 +// This serves as our best estimate of a future unknown.
1.39 +//
1.40 +class AdaptiveWeightedAverage : public CHeapObj {
1.41 + private:
1.42 + float _average; // The last computed average
1.43 + unsigned _sample_count; // How often we've sampled this average
1.44 + unsigned _weight; // The weight used to smooth the averages
1.45 + // A higher weight favors the most
1.46 + // recent data.
1.47 +
1.48 + protected:
1.49 + float _last_sample; // The last value sampled.
1.50 +
1.51 + void increment_count() { _sample_count++; }
1.52 + void set_average(float avg) { _average = avg; }
1.53 +
1.54 + // Helper function, computes an adaptive weighted average
1.55 + // given a sample and the last average
1.56 + float compute_adaptive_average(float new_sample, float average);
1.57 +
1.58 + public:
1.59 + // Input weight must be between 0 and 100
1.60 + AdaptiveWeightedAverage(unsigned weight) :
1.61 + _average(0.0), _sample_count(0), _weight(weight), _last_sample(0.0) {
1.62 + }
1.63 +
1.64 + // Accessors
1.65 + float average() const { return _average; }
1.66 + unsigned weight() const { return _weight; }
1.67 + unsigned count() const { return _sample_count; }
1.68 + float last_sample() const { return _last_sample; }
1.69 +
1.70 + // Update data with a new sample.
1.71 + void sample(float new_sample);
1.72 +
1.73 + static inline float exp_avg(float avg, float sample,
1.74 + unsigned int weight) {
1.75 + assert(0 <= weight && weight <= 100, "weight must be a percent");
1.76 + return (100.0F - weight) * avg / 100.0F + weight * sample / 100.0F;
1.77 + }
1.78 + static inline size_t exp_avg(size_t avg, size_t sample,
1.79 + unsigned int weight) {
1.80 + // Convert to float and back to avoid integer overflow.
1.81 + return (size_t)exp_avg((float)avg, (float)sample, weight);
1.82 + }
1.83 +};
1.84 +
1.85 +
1.86 +// A weighted average that includes a deviation from the average,
1.87 +// some multiple of which is added to the average.
1.88 +//
1.89 +// This serves as our best estimate of an upper bound on a future
1.90 +// unknown.
1.91 +class AdaptivePaddedAverage : public AdaptiveWeightedAverage {
1.92 + private:
1.93 + float _padded_avg; // The last computed padded average
1.94 + float _deviation; // Running deviation from the average
1.95 + unsigned _padding; // A multiple which, added to the average,
1.96 + // gives us an upper bound guess.
1.97 +
1.98 + protected:
1.99 + void set_padded_average(float avg) { _padded_avg = avg; }
1.100 + void set_deviation(float dev) { _deviation = dev; }
1.101 +
1.102 + public:
1.103 + AdaptivePaddedAverage() :
1.104 + AdaptiveWeightedAverage(0),
1.105 + _padded_avg(0.0), _deviation(0.0), _padding(0) {}
1.106 +
1.107 + AdaptivePaddedAverage(unsigned weight, unsigned padding) :
1.108 + AdaptiveWeightedAverage(weight),
1.109 + _padded_avg(0.0), _deviation(0.0), _padding(padding) {}
1.110 +
1.111 + // Placement support
1.112 + void* operator new(size_t ignored, void* p) { return p; }
1.113 + // Allocator
1.114 + void* operator new(size_t size) { return CHeapObj::operator new(size); }
1.115 +
1.116 + // Accessor
1.117 + float padded_average() const { return _padded_avg; }
1.118 + float deviation() const { return _deviation; }
1.119 + unsigned padding() const { return _padding; }
1.120 +
1.121 + // Override
1.122 + void sample(float new_sample);
1.123 +};
1.124 +
1.125 +// A weighted average that includes a deviation from the average,
1.126 +// some multiple of which is added to the average.
1.127 +//
1.128 +// This serves as our best estimate of an upper bound on a future
1.129 +// unknown.
1.130 +// A special sort of padded average: it doesn't update deviations
1.131 +// if the sample is zero. The average is allowed to change. We're
1.132 +// preventing the zero samples from drastically changing our padded
1.133 +// average.
1.134 +class AdaptivePaddedNoZeroDevAverage : public AdaptivePaddedAverage {
1.135 +public:
1.136 + AdaptivePaddedNoZeroDevAverage(unsigned weight, unsigned padding) :
1.137 + AdaptivePaddedAverage(weight, padding) {}
1.138 + // Override
1.139 + void sample(float new_sample);
1.140 +};
1.141 +// Use a least squares fit to a set of data to generate a linear
1.142 +// equation.
1.143 +// y = intercept + slope * x
1.144 +
1.145 +class LinearLeastSquareFit : public CHeapObj {
1.146 + double _sum_x; // sum of all independent data points x
1.147 + double _sum_x_squared; // sum of all independent data points x**2
1.148 + double _sum_y; // sum of all dependent data points y
1.149 + double _sum_xy; // sum of all x * y.
1.150 + double _intercept; // constant term
1.151 + double _slope; // slope
1.152 + // The weighted averages are not currently used but perhaps should
1.153 + // be used to get decaying averages.
1.154 + AdaptiveWeightedAverage _mean_x; // weighted mean of independent variable
1.155 + AdaptiveWeightedAverage _mean_y; // weighted mean of dependent variable
1.156 +
1.157 + public:
1.158 + LinearLeastSquareFit(unsigned weight);
1.159 + void update(double x, double y);
1.160 + double y(double x);
1.161 + double slope() { return _slope; }
1.162 + // Methods to decide if a change in the dependent variable will
1.163 + // achive a desired goal. Note that these methods are not
1.164 + // complementary and both are needed.
1.165 + bool decrement_will_decrease();
1.166 + bool increment_will_decrease();
1.167 +};
1.168 +
1.169 +class GCPauseTimer : StackObj {
1.170 + elapsedTimer* _timer;
1.171 + public:
1.172 + GCPauseTimer(elapsedTimer* timer) {
1.173 + _timer = timer;
1.174 + _timer->stop();
1.175 + }
1.176 + ~GCPauseTimer() {
1.177 + _timer->start();
1.178 + }
1.179 +};
