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-rw-r--r--bench/bench_gemm.cpp271
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diff --git a/bench/bench_gemm.cpp b/bench/bench_gemm.cpp
new file mode 100644
index 000000000..98ac34e20
--- /dev/null
+++ b/bench/bench_gemm.cpp
@@ -0,0 +1,271 @@
+
+// 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
+
+#include <iostream>
+#include <Eigen/Core>
+#include <bench/BenchTimer.h>
+
+using namespace std;
+using namespace Eigen;
+
+#ifndef SCALAR
+// #define SCALAR std::complex<float>
+#define SCALAR float
+#endif
+
+typedef SCALAR Scalar;
+typedef NumTraits<Scalar>::Real RealScalar;
+typedef Matrix<RealScalar,Dynamic,Dynamic> A;
+typedef Matrix</*Real*/Scalar,Dynamic,Dynamic> B;
+typedef Matrix<Scalar,Dynamic,Dynamic> C;
+typedef Matrix<RealScalar,Dynamic,Dynamic> M;
+
+#ifdef HAVE_BLAS
+
+extern "C" {
+ #include <bench/btl/libs/C_BLAS/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;
+
+void blas_gemm(const MatrixXf& a, const MatrixXf& b, MatrixXf& c)
+{
+ int M = c.rows(); int N = c.cols(); int K = a.cols();
+ int lda = a.rows(); int ldb = b.rows(); int ldc = c.rows();
+
+ sgemm_(&notrans,&notrans,&M,&N,&K,&fone,
+ const_cast<float*>(a.data()),&lda,
+ const_cast<float*>(b.data()),&ldb,&fone,
+ c.data(),&ldc);
+}
+
+EIGEN_DONT_INLINE void blas_gemm(const MatrixXd& a, const MatrixXd& b, MatrixXd& c)
+{
+ int M = c.rows(); int N = c.cols(); int K = a.cols();
+ int lda = a.rows(); int ldb = b.rows(); int ldc = c.rows();
+
+ dgemm_(&notrans,&notrans,&M,&N,&K,&done,
+ const_cast<double*>(a.data()),&lda,
+ const_cast<double*>(b.data()),&ldb,&done,
+ c.data(),&ldc);
+}
+
+void blas_gemm(const MatrixXcf& a, const MatrixXcf& b, MatrixXcf& c)
+{
+ int M = c.rows(); int N = c.cols(); int K = a.cols();
+ int lda = a.rows(); int ldb = b.rows(); int ldc = c.rows();
+
+ cgemm_(&notrans,&notrans,&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);
+}
+
+void blas_gemm(const MatrixXcd& a, const MatrixXcd& b, MatrixXcd& c)
+{
+ int M = c.rows(); int N = c.cols(); int K = a.cols();
+ int lda = a.rows(); int ldb = b.rows(); int ldc = c.rows();
+
+ zgemm_(&notrans,&notrans,&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;
+}
+
+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 cache_size = -1;
+
+ bool need_help = false;
+ for (int i=1; i<argc; ++i)
+ {
+ if(argv[i][0]=='s')
+ s = atoi(argv[i]+1);
+ else if(argv[i][0]=='c')
+ cache_size = atoi(argv[i]+1);
+ else if(argv[i][0]=='t')
+ tries = atoi(argv[i]+1);
+ else if(argv[i][0]=='p')
+ rep = atoi(argv[i]+1);
+ else
+ need_help = true;
+ }
+
+ if(need_help)
+ {
+ std::cout << argv[0] << " s<matrix size> c<cache size> t<nb tries> p<nb repeats>\n";
+ return 1;
+ }
+
+ if(cache_size>0)
+ setCpuCacheSizes(cache_size,96*cache_size);
+
+ int m = s;
+ int n = s;
+ int p = s;
+ 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 << "\n";
+
+ C r = c;
+
+ // check the parallel product is correct
+ #if defined EIGEN_HAS_OPENMP
+ 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::cerr << "Warning, your product is crap!\n\n";
+ #else
+ gemm(a,b,c);
+ r.noalias() += a.cast<Scalar>() * b.cast<Scalar>();
+ if(!r.isApprox(c)) 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
+
+ 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::internal::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
+
+ #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;
+}
+
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