Mercurial > hg > truffle
comparison src/share/vm/gc_implementation/shared/gcUtil.cpp @ 0:a61af66fc99e jdk7-b24
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author | duke |
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date | Sat, 01 Dec 2007 00:00:00 +0000 |
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children | e018e6884bd8 |
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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 } |