Mercurial > hg > truffle
annotate src/share/vm/gc_implementation/shared/gcUtil.cpp @ 10374:87c64c0438fb
6976350: G1: deal with fragmentation while copying objects during GC
Summary: Create G1ParGCAllocBufferContainer to contain two buffers instead of previously using one buffer, in order to hold the first priority buffer longer. Thus, when some large objects hits the value of free space left in the first priority buffer it has an alternative to fit in the second priority buffer while the first priority buffer is given more chances to try allocating smaller objects. Overall, it will improve heap space efficiency.
Reviewed-by: johnc, jmasa, brutisso
Contributed-by: tamao <tao.mao@oracle.com>
| author | tamao |
|---|---|
| date | Mon, 03 Jun 2013 14:37:13 -0700 |
| parents | b9a9ed0f8eeb |
| children | 63a4eb8bcd23 |
| rev | line source |
|---|---|
| 0 | 1 /* |
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2 * Copyright (c) 2002, 2012, 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 | |
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34 // used to be 1, the second to be 1/2, etc until we have |
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35 // OLD_THRESHOLD/weight samples. |
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36 unsigned count_weight = 0; |
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37 |
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38 // Avoid division by zero if the counter wraps (7158457) |
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39 if (!is_old()) { |
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40 count_weight = OLD_THRESHOLD/count(); |
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41 } |
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42 |
| 0 | 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 | |
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59 void AdaptiveWeightedAverage::print() const { |
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60 print_on(tty); |
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61 } |
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62 |
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63 void AdaptiveWeightedAverage::print_on(outputStream* st) const { |
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64 guarantee(false, "NYI"); |
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65 } |
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66 |
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67 void AdaptivePaddedAverage::print() const { |
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68 print_on(tty); |
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69 } |
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70 |
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71 void AdaptivePaddedAverage::print_on(outputStream* st) const { |
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72 guarantee(false, "NYI"); |
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73 } |
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74 |
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75 void AdaptivePaddedNoZeroDevAverage::print() const { |
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76 print_on(tty); |
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77 } |
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78 |
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79 void AdaptivePaddedNoZeroDevAverage::print_on(outputStream* st) const { |
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80 guarantee(false, "NYI"); |
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81 } |
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82 |
| 0 | 83 void AdaptivePaddedAverage::sample(float new_sample) { |
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84 // Compute new adaptive weighted average based on new sample. |
| 0 | 85 AdaptiveWeightedAverage::sample(new_sample); |
| 86 | |
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87 // Now update the deviation and the padded average. |
| 0 | 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) : | |
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113 _sum_x(0), _sum_x_squared(0), _sum_y(0), _sum_xy(0), |
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114 _intercept(0), _slope(0), _mean_x(weight), _mean_y(weight) {} |
| 0 | 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 untuitive 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 } |