-
/*
* Copyright (c) 1999-2008 Mark D. Hill and David A. Wood
* All rights reserved.
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
-/*
- * $Id$
- *
- */
+#include <cmath>
+#include <iomanip>
+#include "base/intmath.hh"
#include "mem/ruby/common/Histogram.hh"
-Histogram::Histogram(int binsize, int bins)
+using namespace std;
+
+Histogram::Histogram(int binsize, uint32_t bins)
{
- m_binsize = binsize;
- m_bins = bins;
- clear();
+ m_binsize = binsize;
+ clear(bins);
}
Histogram::~Histogram()
{
}
-void Histogram::clear(int binsize, int bins)
+void
+Histogram::clear(int binsize, uint32_t bins)
{
- m_binsize = binsize;
- clear(bins);
+ m_binsize = binsize;
+ clear(bins);
}
-void Histogram::clear(int bins)
+void
+Histogram::clear(uint32_t bins)
{
- m_bins = bins;
- m_largest_bin = 0;
- m_max = 0;
- m_data.setSize(m_bins);
- for (int i = 0; i < m_bins; i++) {
- m_data[i] = 0;
- }
- m_count = 0;
- m_max = 0;
-
- m_sumSamples = 0;
- m_sumSquaredSamples = 0;
-}
+ m_largest_bin = 0;
+ m_max = 0;
+ m_data.resize(bins);
+ for (uint32_t i = 0; i < bins; i++) {
+ m_data[i] = 0;
+ }
+ m_count = 0;
+ m_max = 0;
+ m_sumSamples = 0;
+ m_sumSquaredSamples = 0;
+}
-void Histogram::add(int64 value)
+void
+Histogram::doubleBinSize()
{
- assert(value >= 0);
- m_max = max(m_max, value);
- m_count++;
-
- m_sumSamples += value;
- m_sumSquaredSamples += (value*value);
-
- int index;
- if (m_binsize == -1) {
- // This is a log base 2 histogram
- if (value == 0) {
- index = 0;
- } else {
- index = int(log(double(value))/log(2.0))+1;
- if (index >= m_data.size()) {
- index = m_data.size()-1;
- }
- }
- } else {
- // This is a linear histogram
- while (m_max >= (m_bins * m_binsize)) {
- for (int i = 0; i < m_bins/2; i++) {
+ assert(m_binsize != -1);
+ uint32_t t_bins = m_data.size();
+
+ for (uint32_t i = 0; i < t_bins/2; i++) {
m_data[i] = m_data[i*2] + m_data[i*2 + 1];
- }
- for (int i = m_bins/2; i < m_bins; i++) {
+ }
+ for (uint32_t i = t_bins/2; i < t_bins; i++) {
m_data[i] = 0;
- }
- m_binsize *= 2;
}
- index = value/m_binsize;
- }
- assert(index >= 0);
- m_data[index]++;
- m_largest_bin = max(m_largest_bin, index);
+
+ m_binsize *= 2;
}
-void Histogram::add(const Histogram& hist)
+void
+Histogram::add(int64 value)
{
- assert(hist.getBins() == m_bins);
- assert(hist.getBinSize() == -1); // assume log histogram
- assert(m_binsize == -1);
+ assert(value >= 0);
+ m_max = max(m_max, value);
+ m_count++;
+
+ m_sumSamples += value;
+ m_sumSquaredSamples += (value*value);
+
+ uint32_t index;
+
+ if (m_binsize == -1) {
+ // This is a log base 2 histogram
+ if (value == 0) {
+ index = 0;
+ } else {
+ index = floorLog2(value) + 1;
+ if (index >= m_data.size()) {
+ index = m_data.size() - 1;
+ }
+ }
+ } else {
+ // This is a linear histogram
+ uint32_t t_bins = m_data.size();
+
+ while (m_max >= (t_bins * m_binsize)) doubleBinSize();
+ index = value/m_binsize;
+ }
- for (int j = 0; j < hist.getData(0); j++) {
- add(0);
- }
+ assert(index < m_data.size());
+ m_data[index]++;
+ m_largest_bin = max(m_largest_bin, index);
+}
- for (int i = 1; i < m_bins; i++) {
- for (int j = 0; j < hist.getData(i); j++) {
- add(1<<(i-1)); // account for the + 1 index
+void
+Histogram::add(Histogram& hist)
+{
+ uint32_t t_bins = m_data.size();
+
+ if (hist.getBins() != t_bins) {
+ if (m_count == 0) {
+ m_data.resize(hist.getBins());
+ } else {
+ fatal("Histograms with different number of bins "
+ "cannot be combined!");
+ }
}
- }
+ m_max = max(m_max, hist.getMax());
+ m_count += hist.size();
+ m_sumSamples += hist.getTotal();
+ m_sumSquaredSamples += hist.getSquaredTotal();
+
+ // Both histograms are log base 2.
+ if (hist.getBinSize() == -1 && m_binsize == -1) {
+ for (int j = 0; j < hist.getData(0); j++) {
+ add(0);
+ }
+
+ for (uint32_t i = 1; i < t_bins; i++) {
+ for (int j = 0; j < hist.getData(i); j++) {
+ add(1<<(i-1)); // account for the + 1 index
+ }
+ }
+ } else if (hist.getBinSize() >= 1 && m_binsize >= 1) {
+ // Both the histogram are linear.
+ // We are assuming that the two histograms have the same
+ // minimum value that they can store.
+
+ while (m_binsize > hist.getBinSize()) hist.doubleBinSize();
+ while (hist.getBinSize() > m_binsize) doubleBinSize();
+
+ assert(m_binsize == hist.getBinSize());
+
+ for (uint32_t i = 0; i < t_bins; i++) {
+ m_data[i] += hist.getData(i);
+
+ if (m_data[i] > 0) m_largest_bin = i;
+ }
+ } else {
+ fatal("Don't know how to combine log and linear histograms!");
+ }
}
// Computation of standard deviation of samples a1, a2, ... aN
// variance = [SUM {ai^2} - (SUM {ai})^2/N]/(N-1)
// std deviation equals square root of variance
-double Histogram::getStandardDeviation() const
+double
+Histogram::getStandardDeviation() const
{
- double variance;
- if(m_count > 1){
- variance = (double)(m_sumSquaredSamples - m_sumSamples*m_sumSamples/m_count)/(m_count - 1);
- } else {
- return 0;
- }
- return sqrt(variance);
+ if (m_count <= 1)
+ return 0.0;
+
+ double variance =
+ (double)(m_sumSquaredSamples - m_sumSamples * m_sumSamples / m_count)
+ / (m_count - 1);
+ return sqrt(variance);
}
-void Histogram::print(ostream& out) const
+void
+Histogram::print(ostream& out) const
{
- printWithMultiplier(out, 1.0);
+ printWithMultiplier(out, 1.0);
}
-void Histogram::printPercent(ostream& out) const
+void
+Histogram::printPercent(ostream& out) const
{
- if (m_count == 0) {
- printWithMultiplier(out, 0.0);
- } else {
- printWithMultiplier(out, 100.0/double(m_count));
- }
+ if (m_count == 0) {
+ printWithMultiplier(out, 0.0);
+ } else {
+ printWithMultiplier(out, 100.0 / double(m_count));
+ }
}
-void Histogram::printWithMultiplier(ostream& out, double multiplier) const
+void
+Histogram::printWithMultiplier(ostream& out, double multiplier) const
{
- if (m_binsize == -1) {
- out << "[binsize: log2 ";
- } else {
- out << "[binsize: " << m_binsize << " ";
- }
- out << "max: " << m_max << " ";
- out << "count: " << m_count << " ";
- // out << "total: " << m_sumSamples << " ";
- if (m_count == 0) {
- out << "average: NaN |";
- out << "standard deviation: NaN |";
- } else {
- out << "average: " << setw(5) << ((double) m_sumSamples)/m_count << " | ";
- out << "standard deviation: " << getStandardDeviation() << " |";
- }
- for (int i = 0; i < m_bins && i <= m_largest_bin; i++) {
- if (multiplier == 1.0) {
- out << " " << m_data[i];
+ if (m_binsize == -1) {
+ out << "[binsize: log2 ";
} else {
- out << " " << double(m_data[i]) * multiplier;
+ out << "[binsize: " << m_binsize << " ";
+ }
+ out << "max: " << m_max << " ";
+ out << "count: " << m_count << " ";
+ // out << "total: " << m_sumSamples << " ";
+ if (m_count == 0) {
+ out << "average: NaN |";
+ out << "standard deviation: NaN |";
+ } else {
+ out << "average: " << setw(5) << ((double) m_sumSamples)/m_count
+ << " | ";
+ out << "standard deviation: " << getStandardDeviation() << " |";
+ }
+
+ for (uint32_t i = 0; i <= m_largest_bin; i++) {
+ if (multiplier == 1.0) {
+ out << " " << m_data[i];
+ } else {
+ out << " " << double(m_data[i]) * multiplier;
+ }
}
- }
- out << " ]";
+ out << " ]";
}
-bool node_less_then_eq(const Histogram* n1, const Histogram* n2)
+bool
+node_less_then_eq(const Histogram* n1, const Histogram* n2)
{
- return (n1->size() > n2->size());
+ return (n1->size() > n2->size());
}
projects / gem5.git / blobdiff