mirror of
https://github.com/paboyle/Grid.git
synced 2025-11-03 21:44:33 +00:00
Batched SGEMM/DGEMM/ZGEMM/CGEMM
Hip, Cuda version and vanilla CPU One MKL stub in comments, to be tested as different.
This commit is contained in:
Peter Boyle
@@ -58,43 +58,42 @@ NAMESPACE_BEGIN(Grid);
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class GridBLAS {
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public:
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static gridblasHandle_t gridblasHandle;
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static int gridblasInit;
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static gridblasHandle_t gridblasHandle;
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static int gridblasInit;
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static void Init(void)
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{
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if ( ! gridblasInit ) {
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static void Init(void)
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{
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if ( ! gridblasInit ) {
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#ifdef GRID_CUDA
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std::cout << "cublasCreate"<<std::endl;
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cublasCreate(&gridblasHandle);
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std::cout << "cublasCreate"<<std::endl;
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cublasCreate(&gridblasHandle);
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#endif
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#ifdef GRID_HIP
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std::cout << "hipblasCreate"<<std::endl;
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hipblasCreate(&gridblasHandle);
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std::cout << "hipblasCreate"<<std::endl;
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hipblasCreate(&gridblasHandle);
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#endif
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#ifdef GRID_SYCL
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#error
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#endif
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}
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}
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}
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}
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// Force construct once
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GridBLAS() { Init(); };
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~GridBLAS() { };
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/////////////////////////////////////////////////////////////////////////////////////
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// BLAS GEMM conventions:
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/////////////////////////////////////////////////////////////////////////////////////
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// - C = alpha A * B + beta C
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// Dimensions:
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// - C_m.n
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// - A_m.k
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// - B_k.n
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// - Flops = 8 M N K
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// - Bytes = 2*sizeof(word) * (MN+MK+KN)
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// M=60, N=12
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// Flop/Byte = 8 . 60.60.12 / (60.12+60.60+60.12)/16 = 4 so expect about 4 TF/s on a GCD
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/////////////////////////////////////////////////////////////////////////////////////
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/////////////////////////////////////////////////////////////////////////////////////
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// BLAS GEMM conventions:
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/////////////////////////////////////////////////////////////////////////////////////
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// - C = alpha A * B + beta C
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// Dimensions:
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// - C_m.n
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// - A_m.k
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// - B_k.n
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// - Flops = 8 M N K
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// - Bytes = 2*sizeof(word) * (MN+MK+KN)
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// M=60, N=12
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// Flop/Byte = 8 . 60.60.12 / (60.12+60.60+60.12)/16 = 4 so expect about 4 TF/s on a GCD
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/////////////////////////////////////////////////////////////////////////////////////
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void synchronise(void)
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{
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#ifdef GRID_HIP
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@@ -110,156 +109,416 @@ public:
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#endif
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}
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void benchmark(int nbasis, int nrhs, int coarseVol, int nstencil)
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{
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int32_t N_A = nbasis*nbasis*coarseVol*nstencil;
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int32_t N_B = nbasis*nrhs*coarseVol*nstencil; // One leg of stencil at a time
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int32_t N_C = nbasis*nrhs*coarseVol*nstencil;
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deviceVector<ComplexD> A(N_A); acceleratorMemSet(&A[0],0,N_A*sizeof(ComplexD));
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deviceVector<ComplexD> B(N_B); acceleratorMemSet(&B[0],0,N_B*sizeof(ComplexD));
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deviceVector<ComplexD> C(N_C); acceleratorMemSet(&C[0],0,N_C*sizeof(ComplexD));
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ComplexD alpha(1.0);
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ComplexD beta (1.0);
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for(int i=0;i<10;i++){
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RealD t0 = usecond();
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for(int s=0;s<nstencil;s++){
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gemmStridedBatched(nbasis,nrhs,nbasis,
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alpha,
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&A[0], // m x k
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&B[0], // k x n
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beta,
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&C[0], // m x n
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coarseVol);
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}
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synchronise();
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RealD t1 = usecond();
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RealD flops = 8.0*nbasis*nbasis*nrhs*coarseVol*nstencil;
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RealD bytes = 1.0*sizeof(ComplexD)*(nbasis*nbasis+nbasis*nrhs*3)*coarseVol*nstencil;
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std::cout << " batched Blas call "<<i<<" "<< flops/(t1-t0)/1.e3 <<" GF/s "<<(t1-t0)/1.e3<<" ms "<<std::endl;
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std::cout << " batched Blas call "<<i<<" "<< bytes/(t1-t0)/1.e3 <<" GB/s "<<(t1-t0)/1.e3<<" ms "<<std::endl;
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}
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}
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{
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int32_t N_A = nbasis*nbasis*coarseVol*nstencil;
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int32_t N_B = nbasis*nrhs*coarseVol*nstencil; // One leg of stencil at a time
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int32_t N_C = nbasis*nrhs*coarseVol*nstencil;
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deviceVector<ComplexD> A(N_A); acceleratorMemSet(&A[0],0,N_A*sizeof(ComplexD));
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deviceVector<ComplexD> B(N_B); acceleratorMemSet(&B[0],0,N_B*sizeof(ComplexD));
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deviceVector<ComplexD> C(N_C); acceleratorMemSet(&C[0],0,N_C*sizeof(ComplexD));
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ComplexD alpha(1.0);
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ComplexD beta (1.0);
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for(int i=0;i<10;i++){
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RealD t0 = usecond();
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for(int s=0;s<nstencil;s++){
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gemmStridedBatched(nbasis,nrhs,nbasis,
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alpha,
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&A[0], // m x k
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&B[0], // k x n
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beta,
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&C[0], // m x n
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coarseVol);
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}
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synchronise();
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RealD t1 = usecond();
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RealD flops = 8.0*nbasis*nbasis*nrhs*coarseVol*nstencil;
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RealD bytes = 1.0*sizeof(ComplexD)*(nbasis*nbasis+nbasis*nrhs*3)*coarseVol*nstencil;
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std::cout << " batched Blas call "<<i<<" "<< flops/(t1-t0)/1.e3 <<" GF/s "<<(t1-t0)/1.e3<<" ms "<<std::endl;
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std::cout << " batched Blas call "<<i<<" "<< bytes/(t1-t0)/1.e3 <<" GB/s "<<(t1-t0)/1.e3<<" ms "<<std::endl;
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}
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}
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void gemmBatched(int m,int n, int k,
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ComplexD alpha,
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deviceVector<ComplexD*> &Amk, // pointer list to matrices
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deviceVector<ComplexD*> &Bkn,
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ComplexD beta,
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deviceVector<ComplexD*> &Cmn)
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{
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RealD t2=usecond();
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int32_t batchCount = Amk.size();
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// Use C-row major storage, so transpose calls
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int lda = m; // m x k column major
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int ldb = k; // k x n column major
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int ldc = m; // m x b column major
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static deviceVector<ComplexD> alpha_p(1);
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static deviceVector<ComplexD> beta_p(1);
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// can prestore the 1 and the zero on device
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acceleratorCopyToDevice((void *)&alpha,(void *)&alpha_p[0],sizeof(ComplexD));
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acceleratorCopyToDevice((void *)&beta ,(void *)&beta_p[0],sizeof(ComplexD));
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RealD t0=usecond();
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void gemmBatched(int m,int n, int k,
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ComplexD alpha,
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deviceVector<ComplexD*> &Amk, // pointer list to matrices
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deviceVector<ComplexD*> &Bkn,
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ComplexD beta,
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deviceVector<ComplexD*> &Cmn)
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{
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RealD t2=usecond();
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int32_t batchCount = Amk.size();
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// Use C-row major storage, so transpose calls
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int lda = m; // m x k column major
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int ldb = k; // k x n column major
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int ldc = m; // m x b column major
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static deviceVector<ComplexD> alpha_p(1);
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static deviceVector<ComplexD> beta_p(1);
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// can prestore the 1 and the zero on device
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acceleratorCopyToDevice((void *)&alpha,(void *)&alpha_p[0],sizeof(ComplexD));
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acceleratorCopyToDevice((void *)&beta ,(void *)&beta_p[0],sizeof(ComplexD));
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RealD t0=usecond();
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// std::cout << "hipblasZgemmBatched mnk "<<m<<","<<n<<","<<k<<" count "<<batchCount<<std::endl;
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assert(Bkn.size()==batchCount);
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assert(Cmn.size()==batchCount);
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#ifdef GRID_HIP
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std::cout << "hipblasZgemmBatched mnk "<<m<<","<<n<<","<<k<<" count "<<batchCount<<std::endl;
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assert(Bkn.size()==batchCount);
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assert(Cmn.size()==batchCount);
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auto err = hipblasZgemmBatched(gridblasHandle,
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HIPBLAS_OP_N,
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HIPBLAS_OP_N,
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m,n,k,
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(hipblasDoubleComplex *) &alpha_p[0],
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(hipblasDoubleComplex **)&Amk[0], lda,
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(hipblasDoubleComplex **)&Bkn[0], ldb,
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(hipblasDoubleComplex *) &beta_p[0],
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(hipblasDoubleComplex **)&Cmn[0], ldc,
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batchCount);
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// std::cout << " hipblas return code " <<(int)err<<std::endl;
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assert(err==HIPBLAS_STATUS_SUCCESS);
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synchronise();
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auto err = hipblasZgemmBatched(gridblasHandle,
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HIPBLAS_OP_N,
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HIPBLAS_OP_N,
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m,n,k,
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(hipblasDoubleComplex *) &alpha_p[0],
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(hipblasDoubleComplex **)&Amk[0], lda,
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(hipblasDoubleComplex **)&Bkn[0], ldb,
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(hipblasDoubleComplex *) &beta_p[0],
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(hipblasDoubleComplex **)&Cmn[0], ldc,
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batchCount);
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// std::cout << " hipblas return code " <<(int)err<<std::endl;
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assert(err==HIPBLAS_STATUS_SUCCESS);
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#endif
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#ifdef GRID_CUDA
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#error "CUDA implemenetation "
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auto err = cublasZgemmBatched(gridblasHandle,
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CUBLAS_OP_N,
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CUBLAS_OP_N,
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m,n,k,
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(cuDoubleComplex *) &alpha_p[0],
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(cuDoubleComplex **)&Amk[0], lda,
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(cuDoubleComplex **)&Bkn[0], ldb,
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(cuDoubleComplex *) &beta_p[0],
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(cuDoubleComplex **)&Cmn[0], ldc,
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batchCount);
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assert(err==CUBLAS_STATUS_SUCCESS);
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#endif
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#ifdef GRID_SYCL
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#error "oneMKL implemenetation "
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//MKL’s cblas_<T>gemm_batch & OneAPI
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#warning "oneMKL implementation not built "
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#endif
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#if !defined(GRID_SYCL) && !defined(GRID_CUDA) && !defined(GRID_HIP)
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// Need a default/reference implementation
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for (int p = 0; p < batchCount; ++p) {
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for (int mm = 0; mm < m; ++mm) {
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for (int nn = 0; nn < n; ++nn) {
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ComplexD c_mn(0.0);
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for (int kk = 0; kk < k, ++kk)
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c_mn += Amk[mm + kk*lda + p*sda] * Bkn[kk + nn*ldb + p*sdb];
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Cmn[mm + nn*ldc + p*sdc] = (*alpha_p)*c_mn + (*beta_p)*Cmn[mm + nn*ldc + p*sdc];
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}
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}
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}
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// Need a default/reference implementation
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for (int p = 0; p < batchCount; ++p) {
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for (int mm = 0; mm < m; ++mm) {
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for (int nn = 0; nn < n; ++nn) {
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ComplexD c_mn(0.0);
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for (int kk = 0; kk < k, ++kk)
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c_mn += Amk[mm + kk*lda + p*sda] * Bkn[kk + nn*ldb + p*sdb];
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Cmn[mm + nn*ldc + p*sdc] = (*alpha_p)*c_mn + (*beta_p)*Cmn[mm + nn*ldc + p*sdc];
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}
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}
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}
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#endif
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synchronise();
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RealD t1=usecond();
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// std::cout << " hipblas synchronised " <<std::endl;
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RealD flops = 8.0*m*n*k*batchCount;
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RealD bytes = 1.0*sizeof(ComplexD)*(m*k+k*n+m*n)*batchCount;
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std::cout << " batched Blas copy "<<(t0-t2)/1.e3 <<" ms "<<std::endl;
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std::cout << " batched Blas call "<<m<<","<<n<<","<<k<<" "<< flops/(t1-t0)/1.e3 <<" GF/s "<<(t1-t0)/1.e3<<" ms "<<std::endl;
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std::cout << " batched Blas call "<<m<<","<<n<<","<<k<<" "<< bytes/(t1-t0)/1.e3 <<" GB/s "<<(t1-t0)/1.e3<<" ms "<<std::endl;
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std::cout <<GridLogPerformance<< " batched Blas copy "<<(t0-t2)/1.e3 <<" ms "<<std::endl;
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std::cout <<GridLogPerformance<< " batched Blas call "<<m<<","<<n<<","<<k<<" "<< flops/(t1-t0)/1.e3 <<" GF/s "<<(t1-t0)/1.e3<<" ms "<<std::endl;
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std::cout <<GridLogPerformance<< " batched Blas call "<<m<<","<<n<<","<<k<<" "<< bytes/(t1-t0)/1.e3 <<" GB/s "<<(t1-t0)/1.e3<<" ms "<<std::endl;
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}
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void gemmStridedBatched(int m,int n, int k,
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ComplexD alpha,
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ComplexD* Amk, // pointer list to matrices
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ComplexD* Bkn,
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ComplexD beta,
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ComplexD* Cmn,
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int batchCount)
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{
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// Use C-row major storage, so transpose calls
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int lda = m; // m x k column major
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int ldb = k; // k x n column major
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int ldc = m; // m x b column major
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int sda = m*k;
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int sdb = k*n;
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int sdc = m*n;
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deviceVector<ComplexD> alpha_p(1);
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deviceVector<ComplexD> beta_p(1);
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acceleratorCopyToDevice((void *)&alpha,(void *)&alpha_p[0],sizeof(ComplexD));
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acceleratorCopyToDevice((void *)&beta ,(void *)&beta_p[0],sizeof(ComplexD));
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void gemmBatched(int m,int n, int k,
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ComplexF alpha,
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deviceVector<ComplexF*> &Amk, // pointer list to matrices
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deviceVector<ComplexF*> &Bkn,
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ComplexF beta,
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deviceVector<ComplexF*> &Cmn)
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{
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RealD t2=usecond();
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int32_t batchCount = Amk.size();
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// Use C-row major storage, so transpose calls
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int lda = m; // m x k column major
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int ldb = k; // k x n column major
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int ldc = m; // m x b column major
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static deviceVector<ComplexF> alpha_p(1);
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static deviceVector<ComplexF> beta_p(1);
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// can prestore the 1 and the zero on device
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acceleratorCopyToDevice((void *)&alpha,(void *)&alpha_p[0],sizeof(ComplexF));
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acceleratorCopyToDevice((void *)&beta ,(void *)&beta_p[0],sizeof(ComplexF));
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RealD t0=usecond();
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// std::cout << "hipblasZgemmBatched mnk "<<m<<","<<n<<","<<k<<" count "<<batchCount<<std::endl;
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assert(Bkn.size()==batchCount);
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assert(Cmn.size()==batchCount);
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#ifdef GRID_HIP
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std::cout << "hipblasZgemmStridedBatched mnk "<<m<<","<<n<<","<<k<<" count "<<batchCount<<std::endl;
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std::cout << "hipblasZgemmStridedBatched ld "<<lda<<","<<ldb<<","<<ldc<<std::endl;
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std::cout << "hipblasZgemmStridedBatched sd "<<sda<<","<<sdb<<","<<sdc<<std::endl;
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{
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auto err = hipblasZgemmStridedBatched(gridblasHandle,
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HIPBLAS_OP_N,
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HIPBLAS_OP_N,
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m,n,k,
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(hipblasDoubleComplex *) &alpha_p[0],
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(hipblasDoubleComplex *) Amk, lda, sda,
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(hipblasDoubleComplex *) Bkn, ldb, sdb,
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(hipblasDoubleComplex *) &beta_p[0],
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(hipblasDoubleComplex *) Cmn, ldc, sdc,
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batchCount);
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std::cout << " hipblas return code " <<(int)err<<std::endl;
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assert(err==HIPBLAS_STATUS_SUCCESS);
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}
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auto err = hipblasCgemmBatched(gridblasHandle,
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HIPBLAS_OP_N,
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HIPBLAS_OP_N,
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m,n,k,
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(hipblasComplex *) &alpha_p[0],
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(hipblasComplex **)&Amk[0], lda,
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(hipblasComplex **)&Bkn[0], ldb,
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(hipblasComplex *) &beta_p[0],
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(hipblasComplex **)&Cmn[0], ldc,
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batchCount);
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// std::cout << " hipblas return code " <<(int)err<<std::endl;
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assert(err==HIPBLAS_STATUS_SUCCESS);
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#endif
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#ifdef GRID_CUDA
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cublasZgemmStridedBatched(gridblasHandle,
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CUBLAS_OP_T,
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CUBLAS_OP_T,
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m,n,k,
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(cuDoubleComplex *)&alpha_p[0],
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(cuDoubleComplex *) Amk, lda, sda,
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(cuDoubleComplex *) Bkn, ldb, sdb,
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(cuDoubleComplex *)&beta_p[],
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(cuDoubleComplex *) Cmn, ldc, sdc,
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batchCount);
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auto err = cublasCgemmBatched(gridblasHandle,
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CUBLAS_OP_N,
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CUBLAS_OP_N,
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m,n,k,
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(cuComplex *) &alpha_p[0],
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(cuComplex **)&Amk[0], lda,
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(cuComplex **)&Bkn[0], ldb,
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(cuComplex *) &beta_p[0],
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(cuComplex **)&Cmn[0], ldc,
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batchCount);
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assert(err==CUBLAS_STATUS_SUCCESS);
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#endif
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#ifdef GRID_SYCL
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#error "oneMKL implemenetation "
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//MKL’s cblas_<T>gemm_batch & OneAPI
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#warning "oneMKL implementation not built "
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#endif
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#if !defined(GRID_SYCL) && !defined(GRID_CUDA) && !defined(GRID_HIP)
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// Need a default/reference implementation
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for (int p = 0; p < batchCount; ++p) {
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for (int mm = 0; mm < m; ++mm) {
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for (int nn = 0; nn < n; ++nn) {
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ComplexD c_mn(0.0);
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for (int kk = 0; kk < k, ++kk)
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c_mn += Amk[mm + kk*lda + p*sda] * Bkn[kk + nn*ldb + p*sdb];
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Cmn[mm + nn*ldc + p*sdc] = (*alpha_p)*c_mn + (*beta_p)*Cmn[mm + nn*ldc + p*sdc];
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}
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}
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}
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#endif
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synchronise();
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||||
RealD t1=usecond();
|
||||
RealD flops = 8.0*m*n*k*batchCount;
|
||||
RealD bytes = 1.0*sizeof(ComplexF)*(m*k+k*n+m*n)*batchCount;
|
||||
std::cout <<GridLogPerformance<< " batched Blas copy "<<(t0-t2)/1.e3 <<" ms "<<std::endl;
|
||||
std::cout <<GridLogPerformance<< " batched Blas call "<<m<<","<<n<<","<<k<<" "<< flops/(t1-t0)/1.e3 <<" GF/s "<<(t1-t0)/1.e3<<" ms "<<std::endl;
|
||||
std::cout <<GridLogPerformance<< " batched Blas call "<<m<<","<<n<<","<<k<<" "<< bytes/(t1-t0)/1.e3 <<" GB/s "<<(t1-t0)/1.e3<<" ms "<<std::endl;
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////
|
||||
// Single precision real GEMM
|
||||
///////////////////////////////////////////////////////////////////////////
|
||||
|
||||
void gemmBatched(int m,int n, int k,
|
||||
RealF alpha,
|
||||
deviceVector<RealF*> &Amk, // pointer list to matrices
|
||||
deviceVector<RealF*> &Bkn,
|
||||
RealF beta,
|
||||
deviceVector<RealF*> &Cmn)
|
||||
{
|
||||
RealD t2=usecond();
|
||||
int32_t batchCount = Amk.size();
|
||||
// Use C-row major storage, so transpose calls
|
||||
int lda = m; // m x k column major
|
||||
int ldb = k; // k x n column major
|
||||
int ldc = m; // m x b column major
|
||||
static deviceVector<RealF> alpha_p(1);
|
||||
static deviceVector<RealF> beta_p(1);
|
||||
// can prestore the 1 and the zero on device
|
||||
acceleratorCopyToDevice((void *)&alpha,(void *)&alpha_p[0],sizeof(RealF));
|
||||
acceleratorCopyToDevice((void *)&beta ,(void *)&beta_p[0],sizeof(RealF));
|
||||
RealD t0=usecond();
|
||||
// std::cout << "hipblasZgemmBatched mnk "<<m<<","<<n<<","<<k<<" count "<<batchCount<<std::endl;
|
||||
assert(Bkn.size()==batchCount);
|
||||
assert(Cmn.size()==batchCount);
|
||||
#ifdef GRID_HIP
|
||||
auto err = hipblasSgemmBatched(gridblasHandle,
|
||||
HIPBLAS_OP_N,
|
||||
HIPBLAS_OP_N,
|
||||
m,n,k,
|
||||
(float *) &alpha_p[0],
|
||||
(float **)&Amk[0], lda,
|
||||
(float **)&Bkn[0], ldb,
|
||||
(float *) &beta_p[0],
|
||||
(float **)&Cmn[0], ldc,
|
||||
batchCount);
|
||||
assert(err==HIPBLAS_STATUS_SUCCESS);
|
||||
#endif
|
||||
#ifdef GRID_CUDA
|
||||
auto err = cublasSgemmBatched(gridblasHandle,
|
||||
CUBLAS_OP_N,
|
||||
CUBLAS_OP_N,
|
||||
m,n,k,
|
||||
(float *) &alpha_p[0],
|
||||
(float **)&Amk[0], lda,
|
||||
(float **)&Bkn[0], ldb,
|
||||
(float *) &beta_p[0],
|
||||
(float **)&Cmn[0], ldc,
|
||||
batchCount);
|
||||
assert(err==CUBLAS_STATUS_SUCCESS);
|
||||
#endif
|
||||
#ifdef GRID_SYCL
|
||||
//MKL’s cblas_<T>gemm_batch & OneAPI
|
||||
#warning "oneMKL implementation not built "
|
||||
#endif
|
||||
#if !defined(GRID_SYCL) && !defined(GRID_CUDA) && !defined(GRID_HIP)
|
||||
// Need a default/reference implementation
|
||||
for (int p = 0; p < batchCount; ++p) {
|
||||
for (int mm = 0; mm < m; ++mm) {
|
||||
for (int nn = 0; nn < n; ++nn) {
|
||||
RealD c_mn(0.0);
|
||||
for (int kk = 0; kk < k, ++kk)
|
||||
c_mn += Amk[mm + kk*lda + p*sda] * Bkn[kk + nn*ldb + p*sdb];
|
||||
Cmn[mm + nn*ldc + p*sdc] = (*alpha_p)*c_mn + (*beta_p)*Cmn[mm + nn*ldc + p*sdc];
|
||||
}
|
||||
}
|
||||
}
|
||||
#endif
|
||||
synchronise();
|
||||
RealD t1=usecond();
|
||||
RealD flops = 8.0*m*n*k*batchCount;
|
||||
RealD bytes = 1.0*sizeof(RealF)*(m*k+k*n+m*n)*batchCount;
|
||||
std::cout <<GridLogPerformance<< " batched Blas copy "<<(t0-t2)/1.e3 <<" ms "<<std::endl;
|
||||
std::cout <<GridLogPerformance<< " batched Blas call "<<m<<","<<n<<","<<k<<" "<< flops/(t1-t0)/1.e3 <<" GF/s "<<(t1-t0)/1.e3<<" ms "<<std::endl;
|
||||
std::cout <<GridLogPerformance<< " batched Blas call "<<m<<","<<n<<","<<k<<" "<< bytes/(t1-t0)/1.e3 <<" GB/s "<<(t1-t0)/1.e3<<" ms "<<std::endl;
|
||||
}
|
||||
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////
|
||||
// Double precision real GEMM
|
||||
///////////////////////////////////////////////////////////////////////////
|
||||
|
||||
void gemmBatched(int m,int n, int k,
|
||||
RealD alpha,
|
||||
deviceVector<RealD*> &Amk, // pointer list to matrices
|
||||
deviceVector<RealD*> &Bkn,
|
||||
RealD beta,
|
||||
deviceVector<RealD*> &Cmn)
|
||||
{
|
||||
RealD t2=usecond();
|
||||
int32_t batchCount = Amk.size();
|
||||
// Use C-row major storage, so transpose calls
|
||||
int lda = m; // m x k column major
|
||||
int ldb = k; // k x n column major
|
||||
int ldc = m; // m x b column major
|
||||
static deviceVector<RealD> alpha_p(1);
|
||||
static deviceVector<RealD> beta_p(1);
|
||||
// can prestore the 1 and the zero on device
|
||||
acceleratorCopyToDevice((void *)&alpha,(void *)&alpha_p[0],sizeof(RealD));
|
||||
acceleratorCopyToDevice((void *)&beta ,(void *)&beta_p[0],sizeof(RealD));
|
||||
RealD t0=usecond();
|
||||
// std::cout << "hipblasZgemmBatched mnk "<<m<<","<<n<<","<<k<<" count "<<batchCount<<std::endl;
|
||||
assert(Bkn.size()==batchCount);
|
||||
assert(Cmn.size()==batchCount);
|
||||
#ifdef GRID_HIP
|
||||
auto err = hipblasDgemmBatched(gridblasHandle,
|
||||
HIPBLAS_OP_N,
|
||||
HIPBLAS_OP_N,
|
||||
m,n,k,
|
||||
(double *) &alpha_p[0],
|
||||
(double **)&Amk[0], lda,
|
||||
(double **)&Bkn[0], ldb,
|
||||
(double *) &beta_p[0],
|
||||
(double **)&Cmn[0], ldc,
|
||||
batchCount);
|
||||
assert(err==HIPBLAS_STATUS_SUCCESS);
|
||||
#endif
|
||||
#ifdef GRID_CUDA
|
||||
auto err = cublasDgemmBatched(gridblasHandle,
|
||||
CUBLAS_OP_N,
|
||||
CUBLAS_OP_N,
|
||||
m,n,k,
|
||||
(double *) &alpha_p[0],
|
||||
(double **)&Amk[0], lda,
|
||||
(double **)&Bkn[0], ldb,
|
||||
(double *) &beta_p[0],
|
||||
(double **)&Cmn[0], ldc,
|
||||
batchCount);
|
||||
assert(err==CUBLAS_STATUS_SUCCESS);
|
||||
#endif
|
||||
#ifdef GRID_SYCL
|
||||
/*
|
||||
int64_t m64=m;
|
||||
int64_t n64=n;
|
||||
int64_t k64=k;
|
||||
int64_t batchCount64=batchCount;
|
||||
oneapi::mkl::blas::column_major::gemm_batch(*theGridAccelerator,
|
||||
onemkl::transpose::N,
|
||||
onemkl::transpose::N,
|
||||
&m64,&n64,&k64,
|
||||
(double *) &alpha_p[0],
|
||||
(double **)&Amk[0], lda,
|
||||
(double **)&Bkn[0], ldb,
|
||||
(double *) &beta_p[0],
|
||||
(double **)&Cmn[0], ldc,
|
||||
1,&batchCount64);
|
||||
*/
|
||||
//MKL’s cblas_<T>gemm_batch & OneAPI
|
||||
#warning "oneMKL implementation not built "
|
||||
#endif
|
||||
#if !defined(GRID_SYCL) && !defined(GRID_CUDA) && !defined(GRID_HIP)
|
||||
// Need a default/reference implementation
|
||||
for (int p = 0; p < batchCount; ++p) {
|
||||
for (int mm = 0; mm < m; ++mm) {
|
||||
for (int nn = 0; nn < n; ++nn) {
|
||||
RealD c_mn(0.0);
|
||||
for (int kk = 0; kk < k, ++kk)
|
||||
c_mn += Amk[mm + kk*lda + p*sda] * Bkn[kk + nn*ldb + p*sdb];
|
||||
Cmn[mm + nn*ldc + p*sdc] = (*alpha_p)*c_mn + (*beta_p)*Cmn[mm + nn*ldc + p*sdc];
|
||||
}
|
||||
}
|
||||
}
|
||||
#endif
|
||||
synchronise();
|
||||
RealD t1=usecond();
|
||||
RealD flops = 8.0*m*n*k*batchCount;
|
||||
RealD bytes = 1.0*sizeof(RealD)*(m*k+k*n+m*n)*batchCount;
|
||||
std::cout <<GridLogPerformance<< " batched Blas copy "<<(t0-t2)/1.e3 <<" ms "<<std::endl;
|
||||
std::cout <<GridLogPerformance<< " batched Blas call "<<m<<","<<n<<","<<k<<" "<< flops/(t1-t0)/1.e3 <<" GF/s "<<(t1-t0)/1.e3<<" ms "<<std::endl;
|
||||
std::cout <<GridLogPerformance<< " batched Blas call "<<m<<","<<n<<","<<k<<" "<< bytes/(t1-t0)/1.e3 <<" GB/s "<<(t1-t0)/1.e3<<" ms "<<std::endl;
|
||||
}
|
||||
|
||||
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
// Strided case used by benchmark, but generally unused in Grid
|
||||
// Keep a code example in double complex, but don't generate the single and real variants for now
|
||||
////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
void gemmStridedBatched(int m,int n, int k,
|
||||
ComplexD alpha,
|
||||
ComplexD* Amk, // pointer list to matrices
|
||||
ComplexD* Bkn,
|
||||
ComplexD beta,
|
||||
ComplexD* Cmn,
|
||||
int batchCount)
|
||||
{
|
||||
// Use C-row major storage, so transpose calls
|
||||
int lda = m; // m x k column major
|
||||
int ldb = k; // k x n column major
|
||||
int ldc = m; // m x b column major
|
||||
int sda = m*k;
|
||||
int sdb = k*n;
|
||||
int sdc = m*n;
|
||||
deviceVector<ComplexD> alpha_p(1);
|
||||
deviceVector<ComplexD> beta_p(1);
|
||||
acceleratorCopyToDevice((void *)&alpha,(void *)&alpha_p[0],sizeof(ComplexD));
|
||||
acceleratorCopyToDevice((void *)&beta ,(void *)&beta_p[0],sizeof(ComplexD));
|
||||
std::cout << "blasZgemmStridedBatched mnk "<<m<<","<<n<<","<<k<<" count "<<batchCount<<std::endl;
|
||||
std::cout << "blasZgemmStridedBatched ld "<<lda<<","<<ldb<<","<<ldc<<std::endl;
|
||||
std::cout << "blasZgemmStridedBatched sd "<<sda<<","<<sdb<<","<<sdc<<std::endl;
|
||||
#ifdef GRID_HIP
|
||||
auto err = hipblasZgemmStridedBatched(gridblasHandle,
|
||||
HIPBLAS_OP_N,
|
||||
HIPBLAS_OP_N,
|
||||
m,n,k,
|
||||
(hipblasDoubleComplex *) &alpha_p[0],
|
||||
(hipblasDoubleComplex *) Amk, lda, sda,
|
||||
(hipblasDoubleComplex *) Bkn, ldb, sdb,
|
||||
(hipblasDoubleComplex *) &beta_p[0],
|
||||
(hipblasDoubleComplex *) Cmn, ldc, sdc,
|
||||
batchCount);
|
||||
assert(err==HIPBLAS_STATUS_SUCCESS);
|
||||
#endif
|
||||
#ifdef GRID_CUDA
|
||||
cublasZgemmStridedBatched(gridblasHandle,
|
||||
CUBLAS_OP_N,
|
||||
CUBLAS_OP_N,
|
||||
m,n,k,
|
||||
(cuDoubleComplex *) &alpha_p[0],
|
||||
(cuDoubleComplex *) Amk, lda, sda,
|
||||
(cuDoubleComplex *) Bkn, ldb, sdb,
|
||||
(cuDoubleComplex *) &beta_p[0],
|
||||
(cuDoubleComplex *) Cmn, ldc, sdc,
|
||||
batchCount);
|
||||
#endif
|
||||
#ifdef GRID_SYCL
|
||||
#warning "oneMKL implementation not made "
|
||||
#endif
|
||||
#if !definte(GRID_SYCL) && !defined(GRID_CUDA) && !defined(GRID_HIP)
|
||||
// Need a default/reference implementation
|
||||
for (int p = 0; p < batchCount; ++p) {
|
||||
for (int mm = 0; mm < m; ++mm) {
|
||||
@@ -273,6 +532,10 @@ public:
|
||||
}
|
||||
#endif
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
};
|
||||
|
||||
NAMESPACE_END(Grid);
|
||||
|
||||
Reference in New Issue
Block a user