Mercurial > hg > truffle
annotate src/share/vm/gc_implementation/shared/gcUtil.cpp @ 1972:f95d63e2154a
6989984: Use standard include model for Hospot
Summary: Replaced MakeDeps and the includeDB files with more standardized solutions.
Reviewed-by: coleenp, kvn, kamg
| author | stefank |
|---|---|
| date | Tue, 23 Nov 2010 13:22:55 -0800 |
| parents | c18cbe5936b8 |
| children | 176d0be30214 |
| rev | line source |
|---|---|
| 0 | 1 /* |
| 1972 | 2 * Copyright (c) 2002, 2010, Oracle and/or its affiliates. All rights reserved. |
| 0 | 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 * | |
|
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19 * Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA |
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20 * or visit www.oracle.com if you need additional information or have any |
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21 * questions. |
| 0 | 22 * |
| 23 */ | |
| 24 | |
| 1972 | 25 #include "precompiled.hpp" |
| 26 #include "gc_implementation/shared/gcUtil.hpp" | |
| 0 | 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 100/weight | |
| 35 // samples. | |
| 36 unsigned count_weight = 100/count(); | |
| 37 unsigned adaptive_weight = (MAX2(weight(), count_weight)); | |
| 38 | |
| 39 float new_avg = exp_avg(average, new_sample, adaptive_weight); | |
| 40 | |
| 41 return new_avg; | |
| 42 } | |
| 43 | |
| 44 void AdaptiveWeightedAverage::sample(float new_sample) { | |
| 45 increment_count(); | |
| 46 assert(count() != 0, | |
| 47 "Wraparound -- history would be incorrectly discarded"); | |
| 48 | |
| 49 // Compute the new weighted average | |
| 50 float new_avg = compute_adaptive_average(new_sample, average()); | |
| 51 set_average(new_avg); | |
| 52 _last_sample = new_sample; | |
| 53 } | |
| 54 | |
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55 void AdaptiveWeightedAverage::print() const { |
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56 print_on(tty); |
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57 } |
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58 |
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59 void AdaptiveWeightedAverage::print_on(outputStream* st) const { |
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60 guarantee(false, "NYI"); |
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61 } |
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62 |
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63 void AdaptivePaddedAverage::print() const { |
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64 print_on(tty); |
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65 } |
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66 |
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67 void AdaptivePaddedAverage::print_on(outputStream* st) const { |
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68 guarantee(false, "NYI"); |
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69 } |
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70 |
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71 void AdaptivePaddedNoZeroDevAverage::print() const { |
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72 print_on(tty); |
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73 } |
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74 |
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75 void AdaptivePaddedNoZeroDevAverage::print_on(outputStream* st) const { |
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76 guarantee(false, "NYI"); |
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77 } |
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78 |
| 0 | 79 void AdaptivePaddedAverage::sample(float new_sample) { |
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80 // Compute new adaptive weighted average based on new sample. |
| 0 | 81 AdaptiveWeightedAverage::sample(new_sample); |
| 82 | |
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83 // Now update the deviation and the padded average. |
| 0 | 84 float new_avg = average(); |
| 85 float new_dev = compute_adaptive_average(fabsd(new_sample - new_avg), | |
| 86 deviation()); | |
| 87 set_deviation(new_dev); | |
| 88 set_padded_average(new_avg + padding() * new_dev); | |
| 89 _last_sample = new_sample; | |
| 90 } | |
| 91 | |
| 92 void AdaptivePaddedNoZeroDevAverage::sample(float new_sample) { | |
| 93 // Compute our parent classes sample information | |
| 94 AdaptiveWeightedAverage::sample(new_sample); | |
| 95 | |
| 96 float new_avg = average(); | |
| 97 if (new_sample != 0) { | |
| 98 // We only create a new deviation if the sample is non-zero | |
| 99 float new_dev = compute_adaptive_average(fabsd(new_sample - new_avg), | |
| 100 deviation()); | |
| 101 | |
| 102 set_deviation(new_dev); | |
| 103 } | |
| 104 set_padded_average(new_avg + padding() * deviation()); | |
| 105 _last_sample = new_sample; | |
| 106 } | |
| 107 | |
| 108 LinearLeastSquareFit::LinearLeastSquareFit(unsigned weight) : | |
| 109 _sum_x(0), _sum_y(0), _sum_xy(0), | |
| 110 _mean_x(weight), _mean_y(weight) {} | |
| 111 | |
| 112 void LinearLeastSquareFit::update(double x, double y) { | |
| 113 _sum_x = _sum_x + x; | |
| 114 _sum_x_squared = _sum_x_squared + x * x; | |
| 115 _sum_y = _sum_y + y; | |
| 116 _sum_xy = _sum_xy + x * y; | |
| 117 _mean_x.sample(x); | |
| 118 _mean_y.sample(y); | |
| 119 assert(_mean_x.count() == _mean_y.count(), "Incorrect count"); | |
| 120 if ( _mean_x.count() > 1 ) { | |
| 121 double slope_denominator; | |
| 122 slope_denominator = (_mean_x.count() * _sum_x_squared - _sum_x * _sum_x); | |
| 123 // Some tolerance should be injected here. A denominator that is | |
| 124 // nearly 0 should be avoided. | |
| 125 | |
| 126 if (slope_denominator != 0.0) { | |
| 127 double slope_numerator; | |
| 128 slope_numerator = (_mean_x.count() * _sum_xy - _sum_x * _sum_y); | |
| 129 _slope = slope_numerator / slope_denominator; | |
| 130 | |
| 131 // The _mean_y and _mean_x are decaying averages and can | |
| 132 // be used to discount earlier data. If they are used, | |
| 133 // first consider whether all the quantities should be | |
| 134 // kept as decaying averages. | |
| 135 // _intercept = _mean_y.average() - _slope * _mean_x.average(); | |
| 136 _intercept = (_sum_y - _slope * _sum_x) / ((double) _mean_x.count()); | |
| 137 } | |
| 138 } | |
| 139 } | |
| 140 | |
| 141 double LinearLeastSquareFit::y(double x) { | |
| 142 double new_y; | |
| 143 | |
| 144 if ( _mean_x.count() > 1 ) { | |
| 145 new_y = (_intercept + _slope * x); | |
| 146 return new_y; | |
| 147 } else { | |
| 148 return _mean_y.average(); | |
| 149 } | |
| 150 } | |
| 151 | |
| 152 // Both decrement_will_decrease() and increment_will_decrease() return | |
| 153 // true for a slope of 0. That is because a change is necessary before | |
| 154 // a slope can be calculated and a 0 slope will, in general, indicate | |
| 155 // that no calculation of the slope has yet been done. Returning true | |
| 156 // for a slope equal to 0 reflects the intuitive expectation of the | |
| 157 // dependence on the slope. Don't use the complement of these functions | |
| 158 // since that untuitive expectation is not built into the complement. | |
| 159 bool LinearLeastSquareFit::decrement_will_decrease() { | |
| 160 return (_slope >= 0.00); | |
| 161 } | |
| 162 | |
| 163 bool LinearLeastSquareFit::increment_will_decrease() { | |
| 164 return (_slope <= 0.00); | |
| 165 } |