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comparison src/share/vm/gc_implementation/shared/gcUtil.cpp @ 0:a61af66fc99e jdk7-b24

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author duke
date Sat, 01 Dec 2007 00:00:00 +0000
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1 /*
2 * Copyright 2002-2005 Sun Microsystems, Inc. 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 Sun Microsystems, Inc., 4150 Network Circle, Santa Clara,
20 * CA 95054 USA or visit www.sun.com if you need additional information or
21 * have any questions.
22 *
23 */
24
25 # include "incls/_precompiled.incl"
26 # include "incls/_gcUtil.cpp.incl"
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
55 void AdaptivePaddedAverage::sample(float new_sample) {
56 // Compute our parent classes sample information
57 AdaptiveWeightedAverage::sample(new_sample);
58
59 // Now compute the deviation and the new padded sample
60 float new_avg = average();
61 float new_dev = compute_adaptive_average(fabsd(new_sample - new_avg),
62 deviation());
63 set_deviation(new_dev);
64 set_padded_average(new_avg + padding() * new_dev);
65 _last_sample = new_sample;
66 }
67
68 void AdaptivePaddedNoZeroDevAverage::sample(float new_sample) {
69 // Compute our parent classes sample information
70 AdaptiveWeightedAverage::sample(new_sample);
71
72 float new_avg = average();
73 if (new_sample != 0) {
74 // We only create a new deviation if the sample is non-zero
75 float new_dev = compute_adaptive_average(fabsd(new_sample - new_avg),
76 deviation());
77
78 set_deviation(new_dev);
79 }
80 set_padded_average(new_avg + padding() * deviation());
81 _last_sample = new_sample;
82 }
83
84 LinearLeastSquareFit::LinearLeastSquareFit(unsigned weight) :
85 _sum_x(0), _sum_y(0), _sum_xy(0),
86 _mean_x(weight), _mean_y(weight) {}
87
88 void LinearLeastSquareFit::update(double x, double y) {
89 _sum_x = _sum_x + x;
90 _sum_x_squared = _sum_x_squared + x * x;
91 _sum_y = _sum_y + y;
92 _sum_xy = _sum_xy + x * y;
93 _mean_x.sample(x);
94 _mean_y.sample(y);
95 assert(_mean_x.count() == _mean_y.count(), "Incorrect count");
96 if ( _mean_x.count() > 1 ) {
97 double slope_denominator;
98 slope_denominator = (_mean_x.count() * _sum_x_squared - _sum_x * _sum_x);
99 // Some tolerance should be injected here. A denominator that is
100 // nearly 0 should be avoided.
101
102 if (slope_denominator != 0.0) {
103 double slope_numerator;
104 slope_numerator = (_mean_x.count() * _sum_xy - _sum_x * _sum_y);
105 _slope = slope_numerator / slope_denominator;
106
107 // The _mean_y and _mean_x are decaying averages and can
108 // be used to discount earlier data. If they are used,
109 // first consider whether all the quantities should be
110 // kept as decaying averages.
111 // _intercept = _mean_y.average() - _slope * _mean_x.average();
112 _intercept = (_sum_y - _slope * _sum_x) / ((double) _mean_x.count());
113 }
114 }
115 }
116
117 double LinearLeastSquareFit::y(double x) {
118 double new_y;
119
120 if ( _mean_x.count() > 1 ) {
121 new_y = (_intercept + _slope * x);
122 return new_y;
123 } else {
124 return _mean_y.average();
125 }
126 }
127
128 // Both decrement_will_decrease() and increment_will_decrease() return
129 // true for a slope of 0. That is because a change is necessary before
130 // a slope can be calculated and a 0 slope will, in general, indicate
131 // that no calculation of the slope has yet been done. Returning true
132 // for a slope equal to 0 reflects the intuitive expectation of the
133 // dependence on the slope. Don't use the complement of these functions
134 // since that untuitive expectation is not built into the complement.
135 bool LinearLeastSquareFit::decrement_will_decrease() {
136 return (_slope >= 0.00);
137 }
138
139 bool LinearLeastSquareFit::increment_will_decrease() {
140 return (_slope <= 0.00);
141 }