1 /* 2 * Copyright (c) 2002, 2015, 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_SHARED_GCUTIL_HPP 26 #define SHARE_VM_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 // 46 class 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), _last_sample(0.0), 77 _is_old(false) { 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. 126 class 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() { return CHeapObj<mtGC>::operator new(size); } 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. 179 class AdaptivePaddedNoZeroDevAverage : public AdaptivePaddedAverage { 180 public: 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 194 195 class 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_VM_GC_SHARED_GCUTIL_HPP