Quelle bench_gemm.cpp Sprache: C

// g++-4.4 bench_gemm.cpp -I .. -O2 -DNDEBUG -lrt -fopenmp && OMP_NUM_THREADS=2  ./a.out
// icpc bench_gemm.cpp -I .. -O3 -DNDEBUG -lrt -openmp  && OMP_NUM_THREADS=2  ./a.out

// Compilation options:
//
// -DSCALAR=std::complex<double>
// -DSCALARA=double or -DSCALARB=double
// -DHAVE_BLAS
// -DDECOUPLED
//

#include <iostream>
#include <bench/BenchTimer.h>
#include <Eigen/Core>

using namespace std;
using namespace Eigen;

#ifndef SCALAR
// #define SCALAR std::complex<float>
#define SCALAR float
#endif

#ifndef SCALARA
#define SCALARA SCALAR
#endif

#ifndef SCALARB
#define SCALARB SCALAR
#endif

#ifdef ROWMAJ_A
const int opt_A = RowMajor;
#else
const int opt_A = ColMajor;
#endif

#ifdef ROWMAJ_B
const int opt_B = RowMajor;
#else
const int opt_B = ColMajor;
#endif

typedef SCALAR Scalar;
typedef NumTraits<Scalar>::Real RealScalar;
typedef Matrix<SCALARA,Dynamic,Dynamic,opt_A> A;
typedef Matrix<SCALARB,Dynamic,Dynamic,opt_B> B;
typedef Matrix<Scalar,Dynamic,Dynamic> C;
typedef Matrix<RealScalar,Dynamic,Dynamic> M;

#ifdef HAVE_BLAS

extern "C" {
  #include <Eigen/src/misc/blas.h>
}

static float fone = 1;
static float fzero = 0;
static double done = 1;
static double szero = 0;
static std::complex<float> cfone = 1;
static std::complex<float> cfzero = 0;
static std::complex<double> cdone = 1;
static std::complex<double> cdzero = 0;
static char notrans = 'N';
static char trans = 'T';
static char nonunit = 'N';
static char lower = 'L';
static char right = 'R';
static int intone = 1;

#ifdef ROWMAJ_A
const char transA = trans;
#else
const char transA = notrans;
#endif

#ifdef ROWMAJ_B
const char transB = trans;
#else
const char transB = notrans;
#endif

template<typename A,typename B>
void blas_gemm(const A& a, const B& b, MatrixXf& c)
{
  int M = c.rows(); int N = c.cols(); int K = a.cols();
  int lda = a.outerStride(); int ldb = b.outerStride(); int ldc = c.rows();

  sgemm_(&transA,&transB,&M,&N,&K,&fone,
         const_cast<float*>(a.data()),&lda,
         const_cast<float*>(b.data()),&ldb,&fone,
         c.data(),&ldc);
}

template<typename A,typename B>
void blas_gemm(const A& a, const B& b, MatrixXd& c)
{
  int M = c.rows(); int N = c.cols(); int K = a.cols();
  int lda = a.outerStride(); int ldb = b.outerStride(); int ldc = c.rows();

  dgemm_(&transA,&transB,&M,&N,&K,&done,
         const_cast<double*>(a.data()),&lda,
         const_cast<double*>(b.data()),&ldb,&done,
         c.data(),&ldc);
}

template<typename A,typename B>
void blas_gemm(const A& a, const B& b, MatrixXcf& c)
{
  int M = c.rows(); int N = c.cols(); int K = a.cols();
  int lda = a.outerStride(); int ldb = b.outerStride(); int ldc = c.rows();

  cgemm_(&transA,&transB,&M,&N,&K,(float*)&cfone,
         const_cast<float*>((const float*)a.data()),&lda,
         const_cast<float*>((const float*)b.data()),&ldb,(float*)&cfone,
         (float*)c.data(),&ldc);
}

template<typename A,typename B>
void blas_gemm(const A& a, const B& b, MatrixXcd& c)
{
  int M = c.rows(); int N = c.cols(); int K = a.cols();
  int lda = a.outerStride(); int ldb = b.outerStride(); int ldc = c.rows();

  zgemm_(&transA,&transB,&M,&N,&K,(double*)&cdone,
         const_cast<double*>((const double*)a.data()),&lda,
         const_cast<double*>((const double*)b.data()),&ldb,(double*)&cdone,
         (double*)c.data(),&ldc);
}

#endif

void matlab_cplx_cplx(const M& ar, const M& ai, const M& br, const M& bi, M& cr, M& ci)
{
  cr.noalias() += ar * br;
  cr.noalias() -= ai * bi;
  ci.noalias() += ar * bi;
  ci.noalias() += ai * br;
  // [cr ci] += [ar ai] * br + [-ai ar] * bi
}

void matlab_real_cplx(const M& a, const M& br, const M& bi, M& cr, M& ci)
{
  cr.noalias() += a * br;
  ci.noalias() += a * bi;
}

void matlab_cplx_real(const M& ar, const M& ai, const M& b, M& cr, M& ci)
{
  cr.noalias() += ar * b;
  ci.noalias() += ai * b;
}

template<typename A, typename B, typename C>
EIGEN_DONT_INLINE void gemm(const A& a, const B& b, C& c)
{
  c.noalias() += a * b;
}

int main(int argc, char ** argv)
{
  std::ptrdiff_t l1 = internal::queryL1CacheSize();
  std::ptrdiff_t l2 = internal::queryTopLevelCacheSize();
  std::cout << "L1 cache size = " << (l1>0 ? l1/1024 : -1) << " KB\n";
  std::cout << "L2/L3 cache size = " << (l2>0 ? l2/1024 : -1) << " KB\n";
  typedef internal::gebp_traits<Scalar,Scalar> Traits;
  std::cout << "Register blocking = " << Traits::mr << " x " << Traits::nr << "\n";

  int rep = 1;    // number of repetitions per try
  int tries = 2;  // number of tries, we keep the best

  int s = 2048;
  int m = s;
  int n = s;
  int p = s;
  int cache_size1=-1, cache_size2=l2, cache_size3 = 0;

  bool need_help = false;
  for (int i=1; i<argc;)
  {
    if(argv[i][0]=='-')
    {
      if(argv[i][1]=='s')
      {
        ++i;
        s = atoi(argv[i++]);
        m = n = p = s;
        if(argv[i][0]!='-')
        {
          n = atoi(argv[i++]);
          p = atoi(argv[i++]);
        }
      }
      else if(argv[i][1]=='c')
      {
        ++i;
        cache_size1 = atoi(argv[i++]);
        if(argv[i][0]!='-')
        {
          cache_size2 = atoi(argv[i++]);
          if(argv[i][0]!='-')
            cache_size3 = atoi(argv[i++]);
        }
      }
      else if(argv[i][1]=='t')
      {
        tries = atoi(argv[++i]);
        ++i;
      }
      else if(argv[i][1]=='p')
      {
        ++i;
        rep = atoi(argv[i++]);
      }
    }
    else
    {
      need_help = true;
      break;
    }
  }

  if(need_help)
  {
    std::cout << argv[0] << " -s -c -t -p \n";
    std::cout << " : size\n";
    std::cout << " : rows columns depth\n";
    return 1;
  }

#if EIGEN_VERSION_AT_LEAST(3,2,90)
  if(cache_size1>0)
    setCpuCacheSizes(cache_size1,cache_size2,cache_size3);
#endif

  A a(m,p); a.setRandom();
  B b(p,n); b.setRandom();
  C c(m,n); c.setOnes();
  C rc = c;

  std::cout << "Matrix sizes = " << m << "x" << p << " * " << p << "x" << n << "\n";
  std::ptrdiff_t mc(m), nc(n), kc(p);
  internal::computeProductBlockingSizes<Scalar,Scalar>(kc, mc, nc);
  std::cout << "blocking size (mc x kc) = " << mc << " x " << kc << " x " << nc << "\n";

  C r = c;

  // check the parallel product is correct
  #if defined EIGEN_HAS_OPENMP
  Eigen::initParallel();
  int procs = omp_get_max_threads();
  if(procs>1)
  {
    #ifdef HAVE_BLAS
    blas_gemm(a,b,r);
    #else
    omp_set_num_threads(1);
    r.noalias() += a * b;
    omp_set_num_threads(procs);
    #endif
    c.noalias() += a * b;
    if(!r.isApprox(c)) std::cerr << "Warning, your parallel product is crap!\n\n";
  }
  #elif defined HAVE_BLAS
    blas_gemm(a,b,r);
    c.noalias() += a * b;
    if(!r.isApprox(c)) {
      std::cout << (r  - c).norm()/r.norm() << "\n";
      std::cerr << "Warning, your product is crap!\n\n";
    }
  #else
    if(1.*m*n*p<2000.*2000*2000)
    {
      gemm(a,b,c);
      r.noalias() += a.cast<Scalar>() .lazyProduct( b.cast<Scalar>() );
      if(!r.isApprox(c)) {
        std::cout << (r  - c).norm()/r.norm() << "\n";
        std::cerr << "Warning, your product is crap!\n\n";
      }
    }
  #endif

  #ifdef HAVE_BLAS
  BenchTimer tblas;
  c = rc;
  BENCH(tblas, tries, rep, blas_gemm(a,b,c));
  std::cout << "blas cpu " << tblas.best(CPU_TIMER)/rep  << "s \t" << (double(m)*n*p*rep*2/tblas.best(CPU_TIMER))*1e-9  <<  " GFLOPS \t(" << tblas.total(CPU_TIMER)  << "s)\n";
  std::cout << "blas real " << tblas.best(REAL_TIMER)/rep << "s \t" << (double(m)*n*p*rep*2/tblas.best(REAL_TIMER))*1e-9 <<  " GFLOPS \t(" << tblas.total(REAL_TIMER) << "s)\n";
  #endif

  // warm start
  if(b.norm()+a.norm()==123.554) std::cout << "\n";

  BenchTimer tmt;
  c = rc;
  BENCH(tmt, tries, rep, gemm(a,b,c));
  std::cout << "eigen cpu " << tmt.best(CPU_TIMER)/rep  << "s \t" << (double(m)*n*p*rep*2/tmt.best(CPU_TIMER))*1e-9  <<  " GFLOPS \t(" << tmt.total(CPU_TIMER)  << "s)\n";
  std::cout << "eigen real " << tmt.best(REAL_TIMER)/rep << "s \t" << (double(m)*n*p*rep*2/tmt.best(REAL_TIMER))*1e-9 <<  " GFLOPS \t(" << tmt.total(REAL_TIMER) << "s)\n";

  #ifdef EIGEN_HAS_OPENMP
  if(procs>1)
  {
    BenchTimer tmono;
    omp_set_num_threads(1);
    Eigen::setNbThreads(1);
    c = rc;
    BENCH(tmono, tries, rep, gemm(a,b,c));
    std::cout << "eigen mono cpu " << tmono.best(CPU_TIMER)/rep  << "s \t" << (double(m)*n*p*rep*2/tmono.best(CPU_TIMER))*1e-9  <<  " GFLOPS \t(" << tmono.total(CPU_TIMER)  << "s)\n";
    std::cout << "eigen mono real " << tmono.best(REAL_TIMER)/rep << "s \t" << (double(m)*n*p*rep*2/tmono.best(REAL_TIMER))*1e-9 <<  " GFLOPS \t(" << tmono.total(REAL_TIMER) << "s)\n";
    std::cout << "mt speed up x" << tmono.best(CPU_TIMER) / tmt.best(REAL_TIMER)  << " => " << (100.0*tmono.best(CPU_TIMER) / tmt.best(REAL_TIMER))/procs << "%\n";
  }
  #endif

  if(1.*m*n*p<30*30*30)
  {
    BenchTimer tmt;
    c = rc;
    BENCH(tmt, tries, rep, c.noalias()+=a.lazyProduct(b));
    std::cout << "lazy cpu " << tmt.best(CPU_TIMER)/rep  << "s \t" << (double(m)*n*p*rep*2/tmt.best(CPU_TIMER))*1e-9  <<  " GFLOPS \t(" << tmt.total(CPU_TIMER)  << "s)\n";
    std::cout << "lazy real " << tmt.best(REAL_TIMER)/rep << "s \t" << (double(m)*n*p*rep*2/tmt.best(REAL_TIMER))*1e-9 <<  " GFLOPS \t(" << tmt.total(REAL_TIMER) << "s)\n";
  }

  #ifdef DECOUPLED
  if((NumTraits<A::Scalar>::IsComplex) && (NumTraits<B::Scalar>::IsComplex))
  {
    M ar(m,p); ar.setRandom();
    M ai(m,p); ai.setRandom();
    M br(p,n); br.setRandom();
    M bi(p,n); bi.setRandom();
    M cr(m,n); cr.setRandom();
    M ci(m,n); ci.setRandom();

    BenchTimer t;
    BENCH(t, tries, rep, matlab_cplx_cplx(ar,ai,br,bi,cr,ci));
    std::cout << "\"matlab\" cpu " << t.best(CPU_TIMER)/rep  << "s \t" << (double(m)*n*p*rep*2/t.best(CPU_TIMER))*1e-9  <<  " GFLOPS \t(" << t.total(CPU_TIMER)  << "s)\n";
    std::cout << "\"matlab\" real " << t.best(REAL_TIMER)/rep << "s \t" << (double(m)*n*p*rep*2/t.best(REAL_TIMER))*1e-9 <<  " GFLOPS \t(" << t.total(REAL_TIMER) << "s)\n";
  }
  if((!NumTraits<A::Scalar>::IsComplex) && (NumTraits<B::Scalar>::IsComplex))
  {
    M a(m,p);  a.setRandom();
    M br(p,n); br.setRandom();
    M bi(p,n); bi.setRandom();
    M cr(m,n); cr.setRandom();
    M ci(m,n); ci.setRandom();

    BenchTimer t;
    BENCH(t, tries, rep, matlab_real_cplx(a,br,bi,cr,ci));
    std::cout << "\"matlab\" cpu " << t.best(CPU_TIMER)/rep  << "s \t" << (double(m)*n*p*rep*2/t.best(CPU_TIMER))*1e-9  <<  " GFLOPS \t(" << t.total(CPU_TIMER)  << "s)\n";
    std::cout << "\"matlab\" real " << t.best(REAL_TIMER)/rep << "s \t" << (double(m)*n*p*rep*2/t.best(REAL_TIMER))*1e-9 <<  " GFLOPS \t(" << t.total(REAL_TIMER) << "s)\n";
  }
  if((NumTraits<A::Scalar>::IsComplex) && (!NumTraits<B::Scalar>::IsComplex))
  {
    M ar(m,p); ar.setRandom();
    M ai(m,p); ai.setRandom();
    M b(p,n);  b.setRandom();
    M cr(m,n); cr.setRandom();
    M ci(m,n); ci.setRandom();

    BenchTimer t;
    BENCH(t, tries, rep, matlab_cplx_real(ar,ai,b,cr,ci));
    std::cout << "\"matlab\" cpu " << t.best(CPU_TIMER)/rep  << "s \t" << (double(m)*n*p*rep*2/t.best(CPU_TIMER))*1e-9  <<  " GFLOPS \t(" << t.total(CPU_TIMER)  << "s)\n";
    std::cout << "\"matlab\" real " << t.best(REAL_TIMER)/rep << "s \t" << (double(m)*n*p*rep*2/t.best(REAL_TIMER))*1e-9 <<  " GFLOPS \t(" << t.total(REAL_TIMER) << "s)\n";
  }
  #endif

  return 0;
}

quality100%

¤ Dauer der Verarbeitung: 0.16 Sekunden (vorverarbeitet) ¤

Wurzel

Suchen

Beweissystem der NASA

Beweissystem Isabelle

NIST Cobol Testsuite

Cephes Mathematical Library

Wiener Entwicklungsmethode

Haftungshinweis

Die Informationen auf dieser Webseite wurden nach bestem Wissen sorgfältig zusammengestellt. Es wird jedoch weder Vollständigkeit, noch Richtigkeit, noch Qualität der bereit gestellten Informationen zugesichert.

Bemerkung:

Die farbliche Syntaxdarstellung ist noch experimentell.