Mercurial > hg > truffle
annotate src/share/vm/utilities/numberSeq.cpp @ 1086:89f1b9ae8991
6898948: G1: forensic instrumentation for out-of-bounds recent_avg_pause_time_ratio()
Summary: Added instrumentation and (temporary) assert in non-product mode; clipped the value when found out-of-bounds in product mode. Fix of original issue will follow collection of data from this instrumentation.
Reviewed-by: jcoomes, tonyp
| author | ysr |
|---|---|
| date | Fri, 13 Nov 2009 11:55:26 -0800 |
| parents | 37f87013dfd8 |
| children | c18cbe5936b8 |
| rev | line source |
|---|---|
| 342 | 1 /* |
| 2 * Copyright 2001-2007 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/_numberSeq.cpp.incl" | |
| 27 | |
| 28 AbsSeq::AbsSeq(double alpha) : | |
| 29 _num(0), _sum(0.0), _sum_of_squares(0.0), | |
| 30 _davg(0.0), _dvariance(0.0), _alpha(alpha) { | |
| 31 } | |
| 32 | |
| 33 void AbsSeq::add(double val) { | |
| 34 if (_num == 0) { | |
| 35 // if the sequence is empty, the davg is the same as the value | |
| 36 _davg = val; | |
| 37 // and the variance is 0 | |
| 38 _dvariance = 0.0; | |
| 39 } else { | |
| 40 // otherwise, calculate both | |
| 41 _davg = (1.0 - _alpha) * val + _alpha * _davg; | |
| 42 double diff = val - _davg; | |
| 43 _dvariance = (1.0 - _alpha) * diff * diff + _alpha * _dvariance; | |
| 44 } | |
| 45 } | |
| 46 | |
| 47 double AbsSeq::avg() const { | |
| 48 if (_num == 0) | |
| 49 return 0.0; | |
| 50 else | |
| 51 return _sum / total(); | |
| 52 } | |
| 53 | |
| 54 double AbsSeq::variance() const { | |
| 55 if (_num <= 1) | |
| 56 return 0.0; | |
| 57 | |
| 58 double x_bar = avg(); | |
| 59 double result = _sum_of_squares / total() - x_bar * x_bar; | |
| 60 if (result < 0.0) { | |
| 61 // due to loss-of-precision errors, the variance might be negative | |
| 62 // by a small bit | |
| 63 | |
| 64 // guarantee(-0.1 < result && result < 0.0, | |
| 65 // "if variance is negative, it should be very small"); | |
| 66 result = 0.0; | |
| 67 } | |
| 68 return result; | |
| 69 } | |
| 70 | |
| 71 double AbsSeq::sd() const { | |
| 72 double var = variance(); | |
| 73 guarantee( var >= 0.0, "variance should not be negative" ); | |
| 74 return sqrt(var); | |
| 75 } | |
| 76 | |
| 77 double AbsSeq::davg() const { | |
| 78 return _davg; | |
| 79 } | |
| 80 | |
| 81 double AbsSeq::dvariance() const { | |
| 82 if (_num <= 1) | |
| 83 return 0.0; | |
| 84 | |
| 85 double result = _dvariance; | |
| 86 if (result < 0.0) { | |
| 87 // due to loss-of-precision errors, the variance might be negative | |
| 88 // by a small bit | |
| 89 | |
| 90 guarantee(-0.1 < result && result < 0.0, | |
| 91 "if variance is negative, it should be very small"); | |
| 92 result = 0.0; | |
| 93 } | |
| 94 return result; | |
| 95 } | |
| 96 | |
| 97 double AbsSeq::dsd() const { | |
| 98 double var = dvariance(); | |
| 99 guarantee( var >= 0.0, "variance should not be negative" ); | |
| 100 return sqrt(var); | |
| 101 } | |
| 102 | |
| 103 NumberSeq::NumberSeq(double alpha) : | |
| 104 AbsSeq(alpha), _maximum(0.0), _last(0.0) { | |
| 105 } | |
| 106 | |
| 107 bool NumberSeq::check_nums(NumberSeq *total, int n, NumberSeq **parts) { | |
| 108 for (int i = 0; i < n; ++i) { | |
| 109 if (parts[i] != NULL && total->num() != parts[i]->num()) | |
| 110 return false; | |
| 111 } | |
| 112 return true; | |
| 113 } | |
| 114 | |
| 115 NumberSeq::NumberSeq(NumberSeq *total, int n, NumberSeq **parts) { | |
| 116 guarantee(check_nums(total, n, parts), "all seq lengths should match"); | |
| 117 double sum = total->sum(); | |
| 118 for (int i = 0; i < n; ++i) { | |
| 119 if (parts[i] != NULL) | |
| 120 sum -= parts[i]->sum(); | |
| 121 } | |
| 122 | |
| 123 _num = total->num(); | |
| 124 _sum = sum; | |
| 125 | |
| 126 // we do not calculate these... | |
| 127 _sum_of_squares = -1.0; | |
| 128 _maximum = -1.0; | |
| 129 _davg = -1.0; | |
| 130 _dvariance = -1.0; | |
| 131 } | |
| 132 | |
| 133 void NumberSeq::add(double val) { | |
| 134 AbsSeq::add(val); | |
| 135 | |
| 136 _last = val; | |
| 137 if (_num == 0) { | |
| 138 _maximum = val; | |
| 139 } else { | |
| 140 if (val > _maximum) | |
| 141 _maximum = val; | |
| 142 } | |
| 143 _sum += val; | |
| 144 _sum_of_squares += val * val; | |
| 145 ++_num; | |
| 146 } | |
| 147 | |
| 148 | |
| 149 TruncatedSeq::TruncatedSeq(int length, double alpha): | |
| 150 AbsSeq(alpha), _length(length), _next(0) { | |
| 151 _sequence = NEW_C_HEAP_ARRAY(double, _length); | |
| 152 for (int i = 0; i < _length; ++i) | |
| 153 _sequence[i] = 0.0; | |
| 154 } | |
| 155 | |
| 156 void TruncatedSeq::add(double val) { | |
| 157 AbsSeq::add(val); | |
| 158 | |
| 159 // get the oldest value in the sequence... | |
| 160 double old_val = _sequence[_next]; | |
| 161 // ...remove it from the sum and sum of squares | |
| 162 _sum -= old_val; | |
| 163 _sum_of_squares -= old_val * old_val; | |
| 164 | |
| 165 // ...and update them with the new value | |
| 166 _sum += val; | |
| 167 _sum_of_squares += val * val; | |
| 168 | |
| 169 // now replace the old value with the new one | |
| 170 _sequence[_next] = val; | |
| 171 _next = (_next + 1) % _length; | |
| 172 | |
| 173 // only increase it if the buffer is not full | |
| 174 if (_num < _length) | |
| 175 ++_num; | |
| 176 | |
| 177 guarantee( variance() > -1.0, "variance should be >= 0" ); | |
| 178 } | |
| 179 | |
| 180 // can't easily keep track of this incrementally... | |
| 181 double TruncatedSeq::maximum() const { | |
| 182 if (_num == 0) | |
| 183 return 0.0; | |
| 184 double ret = _sequence[0]; | |
| 185 for (int i = 1; i < _num; ++i) { | |
| 186 double val = _sequence[i]; | |
| 187 if (val > ret) | |
| 188 ret = val; | |
| 189 } | |
| 190 return ret; | |
| 191 } | |
| 192 | |
| 193 double TruncatedSeq::last() const { | |
| 194 if (_num == 0) | |
| 195 return 0.0; | |
| 196 unsigned last_index = (_next + _length - 1) % _length; | |
| 197 return _sequence[last_index]; | |
| 198 } | |
| 199 | |
| 200 double TruncatedSeq::oldest() const { | |
| 201 if (_num == 0) | |
| 202 return 0.0; | |
| 203 else if (_num < _length) | |
| 204 // index 0 always oldest value until the array is full | |
| 205 return _sequence[0]; | |
| 206 else { | |
| 207 // since the array is full, _next is over the oldest value | |
| 208 return _sequence[_next]; | |
| 209 } | |
| 210 } | |
| 211 | |
| 212 double TruncatedSeq::predict_next() const { | |
| 213 if (_num == 0) | |
| 214 return 0.0; | |
| 215 | |
| 216 double num = (double) _num; | |
| 217 double x_squared_sum = 0.0; | |
| 218 double x_sum = 0.0; | |
| 219 double y_sum = 0.0; | |
| 220 double xy_sum = 0.0; | |
| 221 double x_avg = 0.0; | |
| 222 double y_avg = 0.0; | |
| 223 | |
| 224 int first = (_next + _length - _num) % _length; | |
| 225 for (int i = 0; i < _num; ++i) { | |
| 226 double x = (double) i; | |
| 227 double y = _sequence[(first + i) % _length]; | |
| 228 | |
| 229 x_squared_sum += x * x; | |
| 230 x_sum += x; | |
| 231 y_sum += y; | |
| 232 xy_sum += x * y; | |
| 233 } | |
| 234 x_avg = x_sum / num; | |
| 235 y_avg = y_sum / num; | |
| 236 | |
| 237 double Sxx = x_squared_sum - x_sum * x_sum / num; | |
| 238 double Sxy = xy_sum - x_sum * y_sum / num; | |
| 239 double b1 = Sxy / Sxx; | |
| 240 double b0 = y_avg - b1 * x_avg; | |
| 241 | |
| 242 return b0 + b1 * num; | |
| 243 } | |
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246 // Printing/Debugging Support |
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248 void AbsSeq::dump() { dump_on(gclog_or_tty); } |
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249 |
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250 void AbsSeq::dump_on(outputStream* s) { |
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251 s->print_cr("\t _num = %d, _sum = %7.3f, _sum_of_squares = %7.3f", |
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252 _num, _sum, _sum_of_squares); |
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253 s->print_cr("\t _davg = %7.3f, _dvariance = %7.3f, _alpha = %7.3f", |
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254 _davg, _dvariance, _alpha); |
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255 } |
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256 |
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257 void NumberSeq::dump_on(outputStream* s) { |
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258 AbsSeq::dump_on(s); |
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259 s->print_cr("\t\t _last = %7.3f, _maximum = %7.3f"); |
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260 } |
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261 |
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262 void TruncatedSeq::dump_on(outputStream* s) { |
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263 AbsSeq::dump_on(s); |
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264 s->print_cr("\t\t _length = %d, _next = %d", _length, _next); |
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265 for (int i = 0; i < _length; i++) { |
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266 if (i%5 == 0) { |
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267 s->cr(); |
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268 s->print("\t"); |
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269 } |
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270 s->print("\t[%d]=%7.3f", i, _sequence[i]); |
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271 } |
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272 s->print_cr(""); |
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273 } |