src/mem/ruby/common/Histogram.cc

   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
  35 using namespace std;
  36
  37 Histogram::Histogram(int binsize, int bins)
  38 {
  39     m_binsize = binsize;
  40     m_bins = bins;
  41     clear();
  42 }
  43
  44 Histogram::~Histogram()
  45 {
  46 }
  47
  48 void
  49 Histogram::clear(int binsize, int bins)
  50 {
  51     m_binsize = binsize;
  52     clear(bins);
  53 }
  54
  55 void
  56 Histogram::clear(int bins)
  57 {
  58     m_bins = bins;
  59     m_largest_bin = 0;
  60     m_max = 0;
  61     m_data.setSize(m_bins);
  62     for (int i = 0; i < m_bins; i++) {
  63         m_data[i] = 0;
  64     }
  65     m_count = 0;
  66     m_max = 0;
  67
  68     m_sumSamples = 0;
  69     m_sumSquaredSamples = 0;
  70 }
  71
  72
  73 void
  74 Histogram::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
  83     int index;
  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
  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         }
 105         index = value/m_binsize;
 106     }
 107     assert(index >= 0);
 108     m_data[index]++;
 109     m_largest_bin = max(m_largest_bin, index);
 110 }
 111
 112 void
 113 Histogram::add(const Histogram& hist)
 114 {
 115     assert(hist.getBins() == m_bins);
 116     assert(hist.getBinSize() == -1);  // assume log histogram
 117     assert(m_binsize == -1);
 118
 119     for (int j = 0; j < hist.getData(0); j++) {
 120         add(0);
 121     }
 122
 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
 126         }
 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
 133 double
 134 Histogram::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
 145 void
 146 Histogram::print(ostream& out) const
 147 {
 148     printWithMultiplier(out, 1.0);
 149 }
 150
 151 void
 152 Histogram::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
 161 void
 162 Histogram::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     }
 180     for (int i = 0; i < m_bins && i <= m_largest_bin; i++) {
 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
 190 bool
 191 node_less_then_eq(const Histogram* n1, const Histogram* n2)
 192 {
 193     return (n1->size() > n2->size());
 194 }