#region License Information /* HeuristicLab * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL) * * This file is part of HeuristicLab. * * HeuristicLab is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * HeuristicLab is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with HeuristicLab. If not, see . */ #endregion using System; using System.Diagnostics; using System.Drawing; using System.Threading; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Optimization; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Algorithms.Benchmarks { [Item("Linpack Algorithm", "Linpack benchmarking algorithm.")] [StorableClass] public class LinpackBenchmark : IBenchmark { [Storable] private byte[][] chunk; public byte[][] ChunkData { get { return chunk; } set { chunk = value; } } [Storable] private TimeSpan timeLimit; public TimeSpan TimeLimit { get { return timeLimit; } set { timeLimit = value; } } private bool stopBenchmark; private CancellationToken cancellationToken; public string ItemName { get { return ItemAttribute.GetName(this.GetType()); } } public string ItemDescription { get { return ItemAttribute.GetDescription(this.GetType()); } } public Version ItemVersion { get { return ItemAttribute.GetVersion(this.GetType()); } } public Image ItemImage { get { return HeuristicLab.Common.Resources.VSImageLibrary.Event; } } #region Benchmark Fields private const int DEFAULT_PSIZE = 1500; private double eps_result = 0.0; private double mflops_result = 0.0; private double residn_result = 0.0; private double time_result = 0.0; private double total = 0.0; private Stopwatch sw = new Stopwatch(); #endregion #region Costructors [StorableConstructor] public LinpackBenchmark(bool deserializing) { } public LinpackBenchmark() { } public LinpackBenchmark(LinpackBenchmark original, Cloner cloner) { cloner.RegisterClonedObject(original, this); } #endregion #region Linpack Benchmark // implementation based on Java version: http://www.netlib.org/benchmark/linpackjava/ public void Run(CancellationToken token, ResultCollection results) { cancellationToken = token; stopBenchmark = false; TimeSpan executionTime = new TimeSpan(); bool resultAchieved = false; do { int n = DEFAULT_PSIZE; int ldaa = DEFAULT_PSIZE; int lda = DEFAULT_PSIZE + 1; double[][] a = new double[ldaa][]; double[] b = new double[ldaa]; double[] x = new double[ldaa]; double ops; double norma; double normx; double resid; int i; int info; int[] ipvt = new int[ldaa]; for (i = 0; i < ldaa; i++) { a[i] = new double[lda]; } ops = (2.0e0 * (((double)n) * n * n)) / 3.0 + 2.0 * (n * n); norma = mathGen(a, lda, n, b); if (cancellationToken.IsCancellationRequested) { throw new OperationCanceledException(cancellationToken); } sw.Reset(); sw.Start(); info = dgefa(a, lda, n, ipvt); if (cancellationToken.IsCancellationRequested) { throw new OperationCanceledException(cancellationToken); } dgesl(a, lda, n, ipvt, b, 0); sw.Stop(); total = sw.Elapsed.TotalMilliseconds / 1000; if (cancellationToken.IsCancellationRequested) { throw new OperationCanceledException(cancellationToken); } for (i = 0; i < n; i++) { x[i] = b[i]; } norma = mathGen(a, lda, n, b); for (i = 0; i < n; i++) { b[i] = -b[i]; } dmxpy(n, b, n, lda, x, a); resid = 0.0; normx = 0.0; for (i = 0; i < n; i++) { resid = (resid > abs(b[i])) ? resid : abs(b[i]); normx = (normx > abs(x[i])) ? normx : abs(x[i]); } eps_result = epslon((double)1.0); residn_result = resid / (n * norma * normx * eps_result); residn_result += 0.005; // for rounding residn_result = (int)(residn_result * 100); residn_result /= 100; time_result = total; time_result += 0.005; // for rounding time_result = (int)(time_result * 100); time_result /= 100; mflops_result = ops / (1.0e6 * total); mflops_result += 0.0005; // for rounding mflops_result = (int)(mflops_result * 1000); mflops_result /= 1000; if (!resultAchieved) { results.Add(new Result("Mflops/s", new DoubleValue(mflops_result))); results.Add(new Result("total Mflops/s", new DoubleValue(mflops_result * Environment.ProcessorCount))); resultAchieved = true; } executionTime += sw.Elapsed; if ((timeLimit == null) || (timeLimit.TotalMilliseconds == 0)) stopBenchmark = true; else if (executionTime > timeLimit) stopBenchmark = true; } while (!stopBenchmark); } private double abs(double d) { return (d >= 0) ? d : -d; } private double mathGen(double[][] a, int lda, int n, double[] b) { Random gen; double norma; int init, i, j; init = 1325; norma = 0.0; gen = new Random(init); if (cancellationToken.IsCancellationRequested) { throw new OperationCanceledException(cancellationToken); } // Next two for() statements switched. Solver wants // matrix in column order. --dmd 3/3/97 for (i = 0; i < n; i++) { for (j = 0; j < n; j++) { a[j][i] = gen.NextDouble() - .5; norma = (a[j][i] > norma) ? a[j][i] : norma; } } for (i = 0; i < n; i++) { b[i] = 0.0; } for (j = 0; j < n; j++) { for (i = 0; i < n; i++) { b[i] += a[j][i]; } } return norma; } private int dgefa(double[][] a, int lda, int n, int[] ipvt) { double[] col_k, col_j; double t; int j, k, kp1, l, nm1; int info; if (cancellationToken.IsCancellationRequested) { throw new OperationCanceledException(cancellationToken); } // gaussian elimination with partial pivoting info = 0; nm1 = n - 1; if (nm1 >= 0) { for (k = 0; k < nm1; k++) { col_k = a[k]; kp1 = k + 1; // find l = pivot index l = idamax(n - k, col_k, k, 1) + k; ipvt[k] = l; // zero pivot implies this column already triangularized if (col_k[l] != 0) { // interchange if necessary if (l != k) { t = col_k[l]; col_k[l] = col_k[k]; col_k[k] = t; } if (cancellationToken.IsCancellationRequested) { throw new OperationCanceledException(cancellationToken); } // compute multipliers t = -1.0 / col_k[k]; dscal(n - (kp1), t, col_k, kp1, 1); if (cancellationToken.IsCancellationRequested) { throw new OperationCanceledException(cancellationToken); } // row elimination with column indexing for (j = kp1; j < n; j++) { col_j = a[j]; t = col_j[l]; if (l != k) { col_j[l] = col_j[k]; col_j[k] = t; } daxpy(n - (kp1), t, col_k, kp1, 1, col_j, kp1, 1); } } else { info = k; } } } ipvt[n - 1] = n - 1; if (a[(n - 1)][(n - 1)] == 0) info = n - 1; return info; } private void dgesl(double[][] a, int lda, int n, int[] ipvt, double[] b, int job) { double t; int k, kb, l, nm1, kp1; if (cancellationToken.IsCancellationRequested) { throw new OperationCanceledException(cancellationToken); } nm1 = n - 1; if (job == 0) { // job = 0 , solve a * x = b. first solve l*y = b if (nm1 >= 1) { for (k = 0; k < nm1; k++) { l = ipvt[k]; t = b[l]; if (l != k) { b[l] = b[k]; b[k] = t; } kp1 = k + 1; daxpy(n - (kp1), t, a[k], kp1, 1, b, kp1, 1); } } if (cancellationToken.IsCancellationRequested) { throw new OperationCanceledException(cancellationToken); } // now solve u*x = y for (kb = 0; kb < n; kb++) { k = n - (kb + 1); b[k] /= a[k][k]; t = -b[k]; daxpy(k, t, a[k], 0, 1, b, 0, 1); } } else { // job = nonzero, solve trans(a) * x = b. first solve trans(u)*y = b for (k = 0; k < n; k++) { t = ddot(k, a[k], 0, 1, b, 0, 1); b[k] = (b[k] - t) / a[k][k]; } if (cancellationToken.IsCancellationRequested) { throw new OperationCanceledException(cancellationToken); } // now solve trans(l)*x = y if (nm1 >= 1) { //for (kb = 1; kb < nm1; kb++) { for (kb = 0; kb < nm1; kb++) { k = n - (kb + 1); kp1 = k + 1; b[k] += ddot(n - (kp1), a[k], kp1, 1, b, kp1, 1); l = ipvt[k]; if (l != k) { t = b[l]; b[l] = b[k]; b[k] = t; } } } } } private void daxpy(int n, double da, double[] dx, int dx_off, int incx, double[] dy, int dy_off, int incy) { int i, ix, iy; if (cancellationToken.IsCancellationRequested) { throw new OperationCanceledException(cancellationToken); } if ((n > 0) && (da != 0)) { if (incx != 1 || incy != 1) { // code for unequal increments or equal increments not equal to 1 ix = 0; iy = 0; if (incx < 0) ix = (-n + 1) * incx; if (incy < 0) iy = (-n + 1) * incy; for (i = 0; i < n; i++) { dy[iy + dy_off] += da * dx[ix + dx_off]; ix += incx; iy += incy; } return; } else { // code for both increments equal to 1 for (i = 0; i < n; i++) dy[i + dy_off] += da * dx[i + dx_off]; } } } private double ddot(int n, double[] dx, int dx_off, int incx, double[] dy, int dy_off, int incy) { double dtemp = 0; int i, ix, iy; if (cancellationToken.IsCancellationRequested) { throw new OperationCanceledException(cancellationToken); } if (n > 0) { if (incx != 1 || incy != 1) { // code for unequal increments or equal increments not equal to 1 ix = 0; iy = 0; if (incx < 0) ix = (-n + 1) * incx; if (incy < 0) iy = (-n + 1) * incy; for (i = 0; i < n; i++) { dtemp += dx[ix + dx_off] * dy[iy + dy_off]; ix += incx; iy += incy; } } else { // code for both increments equal to 1 for (i = 0; i < n; i++) dtemp += dx[i + dx_off] * dy[i + dy_off]; } } return (dtemp); } private void dscal(int n, double da, double[] dx, int dx_off, int incx) { int i, nincx; if (cancellationToken.IsCancellationRequested) { throw new OperationCanceledException(cancellationToken); } if (n > 0) { if (incx != 1) { // code for increment not equal to 1 nincx = n * incx; for (i = 0; i < nincx; i += incx) dx[i + dx_off] *= da; } else { // code for increment equal to 1 for (i = 0; i < n; i++) dx[i + dx_off] *= da; } } } private int idamax(int n, double[] dx, int dx_off, int incx) { double dmax, dtemp; int i, ix, itemp = 0; if (cancellationToken.IsCancellationRequested) { throw new OperationCanceledException(cancellationToken); } if (n < 1) { itemp = -1; } else if (n == 1) { itemp = 0; } else if (incx != 1) { // code for increment not equal to 1 dmax = (dx[dx_off] < 0.0) ? -dx[dx_off] : dx[dx_off]; ix = 1 + incx; for (i = 0; i < n; i++) { dtemp = (dx[ix + dx_off] < 0.0) ? -dx[ix + dx_off] : dx[ix + dx_off]; if (dtemp > dmax) { itemp = i; dmax = dtemp; } ix += incx; } } else { // code for increment equal to 1 itemp = 0; dmax = (dx[dx_off] < 0.0) ? -dx[dx_off] : dx[dx_off]; for (i = 0; i < n; i++) { dtemp = (dx[i + dx_off] < 0.0) ? -dx[i + dx_off] : dx[i + dx_off]; if (dtemp > dmax) { itemp = i; dmax = dtemp; } } } return (itemp); } private double epslon(double x) { double a, b, c, eps; a = 4.0e0 / 3.0e0; eps = 0; while (eps == 0) { b = a - 1.0; c = b + b + b; eps = abs(c - 1.0); } return (eps * abs(x)); } private void dmxpy(int n1, double[] y, int n2, int ldm, double[] x, double[][] m) { int j, i; // cleanup odd vector for (j = 0; j < n2; j++) { for (i = 0; i < n1; i++) { y[i] += x[j] * m[j][i]; } } } #endregion #region Clone public IDeepCloneable Clone(Cloner cloner) { return new LinpackBenchmark(this, cloner); } public object Clone() { return Clone(new Cloner()); } #endregion #region Events public event EventHandler ItemImageChanged; protected virtual void OnItemImageChanged() { EventHandler handler = ItemImageChanged; if (handler != null) handler(this, EventArgs.Empty); } public event EventHandler ToStringChanged; protected virtual void OnToStringChanged() { EventHandler handler = ToStringChanged; if (handler != null) handler(this, EventArgs.Empty); } #endregion } }