1 /* 2 * Copyright (c) 2002, 2012, 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 #include "precompiled.hpp" 26 #include "gc_implementation/shared/gcUtil.hpp" 27 28 // Catch-all file for utility classes 29 30 float AdaptiveWeightedAverage::compute_adaptive_average(float new_sample, 31 float average) { 32 // We smooth the samples by not using weight() directly until we've 33 // had enough data to make it meaningful. We'd like the first weight 34 // used to be 1, the second to be 1/2, etc until we have 35 // OLD_THRESHOLD/weight samples. 36 unsigned count_weight = 0; 37 38 // Avoid division by zero if the counter wraps (7158457) 39 if (!is_old()) { 40 count_weight = OLD_THRESHOLD/count(); 41 } 42 43 unsigned adaptive_weight = (MAX2(weight(), count_weight)); 44 45 float new_avg = exp_avg(average, new_sample, adaptive_weight); 46 47 return new_avg; 48 } 49 50 void AdaptiveWeightedAverage::sample(float new_sample) { 51 increment_count(); 52 53 // Compute the new weighted average 54 float new_avg = compute_adaptive_average(new_sample, average()); 55 set_average(new_avg); 56 _last_sample = new_sample; 57 } 58 59 void AdaptiveWeightedAverage::print() const { 60 print_on(tty); 61 } 62 63 void AdaptiveWeightedAverage::print_on(outputStream* st) const { 64 guarantee(false, "NYI"); 65 } 66 67 void AdaptivePaddedAverage::print() const { 68 print_on(tty); 69 } 70 71 void AdaptivePaddedAverage::print_on(outputStream* st) const { 72 guarantee(false, "NYI"); 73 } 74 75 void AdaptivePaddedNoZeroDevAverage::print() const { 76 print_on(tty); 77 } 78 79 void AdaptivePaddedNoZeroDevAverage::print_on(outputStream* st) const { 80 guarantee(false, "NYI"); 81 } 82 83 void AdaptivePaddedAverage::sample(float new_sample) { 84 // Compute new adaptive weighted average based on new sample. 85 AdaptiveWeightedAverage::sample(new_sample); 86 87 // Now update the deviation and the padded average. 88 float new_avg = average(); 89 float new_dev = compute_adaptive_average(fabsd(new_sample - new_avg), 90 deviation()); 91 set_deviation(new_dev); 92 set_padded_average(new_avg + padding() * new_dev); 93 _last_sample = new_sample; 94 } 95 96 void AdaptivePaddedNoZeroDevAverage::sample(float new_sample) { 97 // Compute our parent classes sample information 98 AdaptiveWeightedAverage::sample(new_sample); 99 100 float new_avg = average(); 101 if (new_sample != 0) { 102 // We only create a new deviation if the sample is non-zero 103 float new_dev = compute_adaptive_average(fabsd(new_sample - new_avg), 104 deviation()); 105 106 set_deviation(new_dev); 107 } 108 set_padded_average(new_avg + padding() * deviation()); 109 _last_sample = new_sample; 110 } 111 112 LinearLeastSquareFit::LinearLeastSquareFit(unsigned weight) : 113 _sum_x(0), _sum_x_squared(0), _sum_y(0), _sum_xy(0), 114 _intercept(0), _slope(0), _mean_x(weight), _mean_y(weight) {} 115 116 void LinearLeastSquareFit::update(double x, double y) { 117 _sum_x = _sum_x + x; 118 _sum_x_squared = _sum_x_squared + x * x; 119 _sum_y = _sum_y + y; 120 _sum_xy = _sum_xy + x * y; 121 _mean_x.sample(x); 122 _mean_y.sample(y); 123 assert(_mean_x.count() == _mean_y.count(), "Incorrect count"); 124 if ( _mean_x.count() > 1 ) { 125 double slope_denominator; 126 slope_denominator = (_mean_x.count() * _sum_x_squared - _sum_x * _sum_x); 127 // Some tolerance should be injected here. A denominator that is 128 // nearly 0 should be avoided. 129 130 if (slope_denominator != 0.0) { 131 double slope_numerator; 132 slope_numerator = (_mean_x.count() * _sum_xy - _sum_x * _sum_y); 133 _slope = slope_numerator / slope_denominator; 134 135 // The _mean_y and _mean_x are decaying averages and can 136 // be used to discount earlier data. If they are used, 137 // first consider whether all the quantities should be 138 // kept as decaying averages. 139 // _intercept = _mean_y.average() - _slope * _mean_x.average(); 140 _intercept = (_sum_y - _slope * _sum_x) / ((double) _mean_x.count()); 141 } 142 } 143 } 144 145 double LinearLeastSquareFit::y(double x) { 146 double new_y; 147 148 if ( _mean_x.count() > 1 ) { 149 new_y = (_intercept + _slope * x); 150 return new_y; 151 } else { 152 return _mean_y.average(); 153 } 154 } 155 156 // Both decrement_will_decrease() and increment_will_decrease() return 157 // true for a slope of 0. That is because a change is necessary before 158 // a slope can be calculated and a 0 slope will, in general, indicate 159 // that no calculation of the slope has yet been done. Returning true 160 // for a slope equal to 0 reflects the intuitive expectation of the 161 // dependence on the slope. Don't use the complement of these functions 162 // since that intuitive expectation is not built into the complement. 163 bool LinearLeastSquareFit::decrement_will_decrease() { 164 return (_slope >= 0.00); 165 } 166 167 bool LinearLeastSquareFit::increment_will_decrease() { 168 return (_slope <= 0.00); 169 }