1/* 2 * Copyright (c) 1999-2008 Mark D. Hill and David A. Wood 3 * All rights reserved. 4 * 5 * Redistribution and use in source and binary forms, with or without 6 * modification, are permitted provided that the following conditions are 7 * met: redistributions of source code must retain the above copyright 8 * notice, this list of conditions and the following disclaimer; 9 * redistributions in binary form must reproduce the above copyright 10 * notice, this list of conditions and the following disclaimer in the 11 * documentation and/or other materials provided with the distribution; 12 * neither the name of the copyright holders nor the names of its 13 * contributors may be used to endorse or promote products derived from 14 * this software without specific prior written permission. 15 * 16 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 17 * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 18 * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR 19 * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT 20 * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, 21 * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT 22 * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, 23 * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY 24 * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT 25 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 26 * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 27 */ 28 29#include <cmath> 30#include <iomanip> 31 32#include "base/intmath.hh" 33#include "mem/ruby/common/Histogram.hh" 34 35using namespace std; 36
| 1/* 2 * Copyright (c) 1999-2008 Mark D. Hill and David A. Wood 3 * All rights reserved. 4 * 5 * Redistribution and use in source and binary forms, with or without 6 * modification, are permitted provided that the following conditions are 7 * met: redistributions of source code must retain the above copyright 8 * notice, this list of conditions and the following disclaimer; 9 * redistributions in binary form must reproduce the above copyright 10 * notice, this list of conditions and the following disclaimer in the 11 * documentation and/or other materials provided with the distribution; 12 * neither the name of the copyright holders nor the names of its 13 * contributors may be used to endorse or promote products derived from 14 * this software without specific prior written permission. 15 * 16 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 17 * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 18 * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR 19 * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT 20 * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, 21 * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT 22 * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, 23 * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY 24 * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT 25 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 26 * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 27 */ 28 29#include <cmath> 30#include <iomanip> 31 32#include "base/intmath.hh" 33#include "mem/ruby/common/Histogram.hh" 34 35using namespace std; 36
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37Histogram::Histogram(int binsize, int bins)
| 37Histogram::Histogram(int binsize, uint32_t bins)
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38{ 39 m_binsize = binsize;
| 38{ 39 m_binsize = binsize;
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40 m_bins = bins; 41 clear();
| 40 clear(bins);
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42} 43 44Histogram::~Histogram() 45{ 46} 47 48void
| 41} 42 43Histogram::~Histogram() 44{ 45} 46 47void
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49Histogram::clear(int binsize, int bins)
| 48Histogram::clear(int binsize, uint32_t bins)
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50{ 51 m_binsize = binsize; 52 clear(bins); 53} 54 55void
| 49{ 50 m_binsize = binsize; 51 clear(bins); 52} 53 54void
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56Histogram::clear(int bins)
| 55Histogram::clear(uint32_t bins)
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57{
| 56{
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58 m_bins = bins;
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59 m_largest_bin = 0; 60 m_max = 0;
| 57 m_largest_bin = 0; 58 m_max = 0;
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61 m_data.resize(m_bins); 62 for (int i = 0; i < m_bins; i++) {
| 59 m_data.resize(bins); 60 for (uint32_t i = 0; i < bins; i++) {
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63 m_data[i] = 0; 64 }
| 61 m_data[i] = 0; 62 }
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| 63
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65 m_count = 0; 66 m_max = 0;
| 64 m_count = 0; 65 m_max = 0;
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67
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68 m_sumSamples = 0; 69 m_sumSquaredSamples = 0; 70} 71
| 66 m_sumSamples = 0; 67 m_sumSquaredSamples = 0; 68} 69
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| 70void 71Histogram::doubleBinSize() 72{ 73 assert(m_binsize != -1); 74 uint32_t t_bins = m_data.size();
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72
| 75
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| 76 for (uint32_t i = 0; i < t_bins/2; i++) { 77 m_data[i] = m_data[i*2] + m_data[i*2 + 1]; 78 } 79 for (uint32_t i = t_bins/2; i < t_bins; i++) { 80 m_data[i] = 0; 81 } 82 83 m_binsize *= 2; 84} 85
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73void 74Histogram::add(int64 value) 75{ 76 assert(value >= 0); 77 m_max = max(m_max, value); 78 m_count++; 79 80 m_sumSamples += value; 81 m_sumSquaredSamples += (value*value); 82
| 86void 87Histogram::add(int64 value) 88{ 89 assert(value >= 0); 90 m_max = max(m_max, value); 91 m_count++; 92 93 m_sumSamples += value; 94 m_sumSquaredSamples += (value*value); 95
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83 int index;
| 96 uint32_t index; 97
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84 if (m_binsize == -1) { 85 // This is a log base 2 histogram 86 if (value == 0) { 87 index = 0; 88 } else { 89 index = floorLog2(value) + 1; 90 if (index >= m_data.size()) { 91 index = m_data.size() - 1; 92 } 93 } 94 } else { 95 // This is a linear histogram
| 98 if (m_binsize == -1) { 99 // This is a log base 2 histogram 100 if (value == 0) { 101 index = 0; 102 } else { 103 index = floorLog2(value) + 1; 104 if (index >= m_data.size()) { 105 index = m_data.size() - 1; 106 } 107 } 108 } else { 109 // This is a linear histogram
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96 while (m_max >= (m_bins * m_binsize)) { 97 for (int i = 0; i < m_bins/2; i++) { 98 m_data[i] = m_data[i*2] + m_data[i*2 + 1]; 99 } 100 for (int i = m_bins/2; i < m_bins; i++) { 101 m_data[i] = 0; 102 } 103 m_binsize *= 2; 104 }
| 110 uint32_t t_bins = m_data.size(); 111 112 while (m_max >= (t_bins * m_binsize)) doubleBinSize();
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105 index = value/m_binsize; 106 }
| 113 index = value/m_binsize; 114 }
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107 assert(index >= 0);
| 115 116 assert(index < m_data.size());
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108 m_data[index]++; 109 m_largest_bin = max(m_largest_bin, index); 110} 111 112void
| 117 m_data[index]++; 118 m_largest_bin = max(m_largest_bin, index); 119} 120 121void
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113Histogram::add(const Histogram& hist)
| 122Histogram::add(Histogram& hist)
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114{
| 123{
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115 assert(hist.getBins() == m_bins); 116 assert(hist.getBinSize() == -1); // assume log histogram 117 assert(m_binsize == -1);
| 124 uint32_t t_bins = m_data.size();
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118
| 125
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119 for (int j = 0; j < hist.getData(0); j++) { 120 add(0);
| 126 if (hist.getBins() != t_bins) { 127 fatal("Histograms with different number of bins cannot be combined!");
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121 } 122
| 128 } 129
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123 for (int i = 1; i < m_bins; i++) { 124 for (int j = 0; j < hist.getData(i); j++) { 125 add(1<<(i-1)); // account for the + 1 index
| 130 m_max = max(m_max, hist.getMax()); 131 m_count += hist.size(); 132 m_sumSamples += hist.getTotal(); 133 m_sumSquaredSamples += hist.getSquaredTotal(); 134 135 // Both histograms are log base 2. 136 if (hist.getBinSize() == -1 && m_binsize == -1) { 137 for (int j = 0; j < hist.getData(0); j++) { 138 add(0);
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126 }
| 139 }
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| 140 141 for (uint32_t i = 1; i < t_bins; i++) { 142 for (int j = 0; j < hist.getData(i); j++) { 143 add(1<<(i-1)); // account for the + 1 index 144 } 145 } 146 } else if (hist.getBinSize() >= 1 && m_binsize >= 1) { 147 // Both the histogram are linear. 148 // We are assuming that the two histograms have the same 149 // minimum value that they can store. 150 151 while (m_binsize > hist.getBinSize()) hist.doubleBinSize(); 152 while (hist.getBinSize() > m_binsize) doubleBinSize(); 153 154 assert(m_binsize == hist.getBinSize()); 155 156 for (uint32_t i = 0; i < t_bins; i++) { 157 m_data[i] += hist.getData(i); 158 159 if (m_data[i] > 0) m_largest_bin = i; 160 } 161 } else { 162 fatal("Don't know how to combine log and linear histograms!");
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127 } 128} 129 130// Computation of standard deviation of samples a1, a2, ... aN 131// variance = [SUM {ai^2} - (SUM {ai})^2/N]/(N-1) 132// std deviation equals square root of variance 133double 134Histogram::getStandardDeviation() const 135{ 136 if (m_count <= 1) 137 return 0.0; 138 139 double variance = 140 (double)(m_sumSquaredSamples - m_sumSamples * m_sumSamples / m_count) 141 / (m_count - 1); 142 return sqrt(variance); 143} 144 145void 146Histogram::print(ostream& out) const 147{ 148 printWithMultiplier(out, 1.0); 149} 150 151void 152Histogram::printPercent(ostream& out) const 153{ 154 if (m_count == 0) { 155 printWithMultiplier(out, 0.0); 156 } else { 157 printWithMultiplier(out, 100.0 / double(m_count)); 158 } 159} 160 161void 162Histogram::printWithMultiplier(ostream& out, double multiplier) const 163{ 164 if (m_binsize == -1) { 165 out << "[binsize: log2 "; 166 } else { 167 out << "[binsize: " << m_binsize << " "; 168 } 169 out << "max: " << m_max << " "; 170 out << "count: " << m_count << " "; 171 // out << "total: " << m_sumSamples << " "; 172 if (m_count == 0) { 173 out << "average: NaN |"; 174 out << "standard deviation: NaN |"; 175 } else { 176 out << "average: " << setw(5) << ((double) m_sumSamples)/m_count 177 << " | "; 178 out << "standard deviation: " << getStandardDeviation() << " |"; 179 }
| 163 } 164} 165 166// Computation of standard deviation of samples a1, a2, ... aN 167// variance = [SUM {ai^2} - (SUM {ai})^2/N]/(N-1) 168// std deviation equals square root of variance 169double 170Histogram::getStandardDeviation() const 171{ 172 if (m_count <= 1) 173 return 0.0; 174 175 double variance = 176 (double)(m_sumSquaredSamples - m_sumSamples * m_sumSamples / m_count) 177 / (m_count - 1); 178 return sqrt(variance); 179} 180 181void 182Histogram::print(ostream& out) const 183{ 184 printWithMultiplier(out, 1.0); 185} 186 187void 188Histogram::printPercent(ostream& out) const 189{ 190 if (m_count == 0) { 191 printWithMultiplier(out, 0.0); 192 } else { 193 printWithMultiplier(out, 100.0 / double(m_count)); 194 } 195} 196 197void 198Histogram::printWithMultiplier(ostream& out, double multiplier) const 199{ 200 if (m_binsize == -1) { 201 out << "[binsize: log2 "; 202 } else { 203 out << "[binsize: " << m_binsize << " "; 204 } 205 out << "max: " << m_max << " "; 206 out << "count: " << m_count << " "; 207 // out << "total: " << m_sumSamples << " "; 208 if (m_count == 0) { 209 out << "average: NaN |"; 210 out << "standard deviation: NaN |"; 211 } else { 212 out << "average: " << setw(5) << ((double) m_sumSamples)/m_count 213 << " | "; 214 out << "standard deviation: " << getStandardDeviation() << " |"; 215 }
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180 for (int i = 0; i < m_bins && i <= m_largest_bin; i++) {
| 216 217 for (uint32_t i = 0; i <= m_largest_bin; i++) {
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181 if (multiplier == 1.0) { 182 out << " " << m_data[i]; 183 } else { 184 out << " " << double(m_data[i]) * multiplier; 185 } 186 } 187 out << " ]"; 188} 189 190bool 191node_less_then_eq(const Histogram* n1, const Histogram* n2) 192{ 193 return (n1->size() > n2->size()); 194}
| 218 if (multiplier == 1.0) { 219 out << " " << m_data[i]; 220 } else { 221 out << " " << double(m_data[i]) * multiplier; 222 } 223 } 224 out << " ]"; 225} 226 227bool 228node_less_then_eq(const Histogram* n1, const Histogram* n2) 229{ 230 return (n1->size() > n2->size()); 231}
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