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Grid/BLAS_benchmark/BatchBlasBench.cc

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Blas benchmark committed stand alone
2024-08-20 17:18:43 +01:00
#include <cassert>
#include <complex>
#include <memory>
#include <vector>
#include <algorithm>
#include <array>
#include <string>
#include <stdio.h>
#include <stdlib.h>
#include <strings.h>
#include <ctime>
#include <iostream>
#include <sys/time.h>
#ifdef GRID_HIP
#include <hipblas/hipblas.h>
#endif
#ifdef GRID_CUDA
#include <cublas_v2.h>
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Blas benchmark committed stand alone
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#endif
#ifdef GRID_SYCL
#include <oneapi/mkl.hpp>
#endif
#ifdef GRID_SYCL
#include <sycl/CL/sycl.hpp>
#include <sycl/usm.hpp>
cl::sycl::queue *theAccelerator;
void acceleratorInit(void)
{
int nDevices = 1;
cl::sycl::gpu_selector selector;
cl::sycl::device selectedDevice { selector };
theAccelerator = new sycl::queue (selectedDevice);
auto name = theAccelerator->get_device().get_info<sycl::info::device::name>();
printf("AcceleratorSyclInit: Selected device is %s\n",name.c_str()); fflush(stdout);
}
inline void *acceleratorAllocDevice(size_t bytes){ return malloc_device(bytes,*theAccelerator);};
inline void acceleratorFreeDevice(void *ptr){free(ptr,*theAccelerator);};
inline void acceleratorFreeDevice(void *ptr,size_t bytes){free(ptr,*theAccelerator);};
inline void acceleratorMemSet(void *base,int value,size_t bytes) { theAccelerator->memset(base,value,bytes); theAccelerator->wait();}
inline void acceleratorCopyToDevice(void *from,void *to,size_t bytes) { theAccelerator->memcpy(to,from,bytes); theAccelerator->wait();}
inline void acceleratorCopyFromDevice(void *from,void *to,size_t bytes){ theAccelerator->memcpy(to,from,bytes); theAccelerator->wait();}
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#define accelerator_barrier(dummy) { theAccelerator->wait(); }
#endif
#ifdef GRID_HIP
hipStream_t copyStream;
hipStream_t computeStream;
void acceleratorInit(void)
{
int device = 0;
auto discard = hipSetDevice(device);
discard = hipStreamCreate(&copyStream);
discard = hipStreamCreate(&computeStream);
printf("AcceleratorHIPInit\n");
}
inline void *acceleratorAllocDevice(size_t bytes)
{
void *ptr=NULL;
auto err = hipMalloc((void **)&ptr,bytes);
if( err != hipSuccess ) {
ptr = (void *) NULL;
fprintf(stderr," hipMalloc failed for %ld %s \n",bytes,hipGetErrorString(err)); fflush(stderr);
}
return ptr;
};
inline void acceleratorFreeDevice(void *ptr,size_t bytes){ auto discard=hipFree(ptr);};
inline void acceleratorFreeDevice(void *ptr){ auto discard=hipFree(ptr);};
inline void acceleratorMemSet(void *base,int value,size_t bytes) { auto discard=hipMemset(base,value,bytes);}
inline void acceleratorCopyToDevice(void *from,void *to,size_t bytes) { auto discard=hipMemcpy(to,from,bytes, hipMemcpyHostToDevice);}
inline void acceleratorCopyFromDevice(void *from,void *to,size_t bytes){ auto discard=hipMemcpy(to,from,bytes, hipMemcpyDeviceToHost);}
#define accelerator_barrier(dummy) \
{ \
auto tmp=hipStreamSynchronize(computeStream); \
auto err = hipGetLastError(); \
if ( err != hipSuccess ) { \
printf("After hipDeviceSynchronize() : HIP error %s \n", hipGetErrorString( err )); \
puts(__FILE__); \
printf("Line %d\n",__LINE__); \
exit(0); \
} \
}
#endif
#ifdef GRID_CUDA
cudaStream_t copyStream;
cudaStream_t computeStream;
void acceleratorInit(void)
{
int device = 0;
cudaSetDevice(device);
cudaStreamCreate(&copyStream);
cudaStreamCreate(&computeStream);
}
inline void *acceleratorAllocDevice(size_t bytes)
{
void *ptr=NULL;
auto err = cudaMalloc((void **)&ptr,bytes);
if( err != cudaSuccess ) {
ptr = (void *) NULL;
printf(" cudaMalloc failed for %d %s \n",bytes,cudaGetErrorString(err));
}
return ptr;
};
inline void acceleratorFreeShared(void *ptr){ cudaFree(ptr);};
inline void acceleratorFreeDevice(void *ptr){ cudaFree(ptr);};
inline void acceleratorCopyToDevice(void *from,void *to,size_t bytes) { cudaMemcpy(to,from,bytes, cudaMemcpyHostToDevice);}
inline void acceleratorCopyFromDevice(void *from,void *to,size_t bytes){ cudaMemcpy(to,from,bytes, cudaMemcpyDeviceToHost);}
inline void acceleratorMemSet(void *base,int value,size_t bytes) { cudaMemset(base,value,bytes);}
#define accelerator_barrier(dummy) \
{ \
cudaStreamSynchronize(computeStream); \
cudaError err = cudaGetLastError(); \
if ( cudaSuccess != err ) { \
printf("accelerator_barrier(): Cuda error %s \n", \
cudaGetErrorString( err )); \
printf("File %s Line %d\n",__FILE__,__LINE__); \
fflush(stdout); \
if (acceleratorAbortOnGpuError) assert(err==cudaSuccess); \
} \
}
#endif
Blas benchmark committed stand alone
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template<class T> void acceleratorPut(T& dev,T&host)
{
acceleratorCopyToDevice(&host,&dev,sizeof(T));
}
template<class T> T acceleratorGet(T& dev)
{
T host;
acceleratorCopyFromDevice(&dev,&host,sizeof(T));
return host;
}
/**************************************************************
* Allocator
**************************************************************
*/
template<typename _Tp>
class devAllocator {
public:
typedef std::size_t size_type;
typedef std::ptrdiff_t difference_type;
typedef _Tp* pointer;
typedef const _Tp* const_pointer;
typedef _Tp& reference;
typedef const _Tp& const_reference;
typedef _Tp value_type;
template<typename _Tp1> struct rebind { typedef devAllocator<_Tp1> other; };
devAllocator() throw() { }
devAllocator(const devAllocator&) throw() { }
template<typename _Tp1> devAllocator(const devAllocator<_Tp1>&) throw() { }
~devAllocator() throw() { }
pointer address(reference __x) const { return &__x; }
size_type max_size() const throw() { return size_t(-1) / sizeof(_Tp); }
pointer allocate(size_type __n, const void* _p= 0)
{
size_type bytes = __n*sizeof(_Tp);
_Tp *ptr = (_Tp*) acceleratorAllocDevice(bytes);
if ( (_Tp*)ptr == (_Tp *) NULL ) {
printf("Grid Device Allocator got NULL for %lu bytes\n",(unsigned long) bytes );
}
assert( ( (_Tp*)ptr != (_Tp *)NULL ) );
return ptr;
}
void deallocate(pointer __p, size_type __n)
{
size_type bytes = __n * sizeof(_Tp);
acceleratorFreeDevice((void *)__p,bytes);
}
void construct(pointer __p, const _Tp& __val) { };
void construct(pointer __p) { };
void destroy(pointer __p) { };
};
template<class T> using deviceVector = std::vector<T,devAllocator<T> >;
/**************************************************************
* Microsecond timer
**************************************************************
*/
inline double usecond(void) {
struct timeval tv;
gettimeofday(&tv,NULL);
return 1.0e6*tv.tv_sec + 1.0*tv.tv_usec;
}
typedef float RealF;
typedef double RealD;
typedef std::complex<float> ComplexF;
typedef std::complex<double> ComplexD;
///////////////////////////////////////////////////////////////////////
// Need to rearrange lattice data to be in the right format for a
// batched multiply. Might as well make these static, dense packed
///////////////////////////////////////////////////////////////////////
#ifdef GRID_HIP
typedef hipblasHandle_t gridblasHandle_t;
#endif
#ifdef GRID_CUDA
typedef cublasHandle_t gridblasHandle_t;
#endif
#ifdef GRID_SYCL
typedef cl::sycl::queue *gridblasHandle_t;
#endif
#ifdef GRID_ONE_MKL
typedef cl::sycl::queue *gridblasHandle_t;
#endif
#if !defined(GRID_SYCL) && !defined(GRID_CUDA) && !defined(GRID_HIP) && !defined(GRID_ONE_MKL)
typedef int32_t gridblasHandle_t;
#endif
enum GridBLASOperation_t { GridBLAS_OP_N, GridBLAS_OP_T, GridBLAS_OP_C } ;
class GridBLAS {
public:
static gridblasHandle_t gridblasHandle;
static int gridblasInit;
static void Init(void)
{
if ( ! gridblasInit ) {
#ifdef GRID_CUDA
std::cout << "cublasCreate"<<std::endl;
cublasCreate(&gridblasHandle);
cublasSetPointerMode(gridblasHandle, CUBLAS_POINTER_MODE_DEVICE);
#endif
#ifdef GRID_HIP
std::cout << "hipblasCreate"<<std::endl;
hipblasCreate(&gridblasHandle);
#endif
#ifdef GRID_SYCL
gridblasHandle = theAccelerator;
#endif
#ifdef GRID_ONE_MKL
cl::sycl::gpu_selector selector;
cl::sycl::device selectedDevice { selector };
cl::sycl::property_list q_prop{cl::sycl::property::queue::in_order()};
gridblasHandle =new sycl::queue (selectedDevice,q_prop);
#endif
gridblasInit=1;
}
}
// Force construct once
GridBLAS() { Init(); };
~GridBLAS() { };
/////////////////////////////////////////////////////////////////////////////////////
// BLAS GEMM conventions:
/////////////////////////////////////////////////////////////////////////////////////
// - C = alpha A * B + beta C
// Dimensions:
// - C_m.n
// - A_m.k
// - B_k.n
// - Flops = 8 M N K
// - Bytes = 2*sizeof(word) * (MN+MK+KN)
// M=60, N=12
// Flop/Byte = 8 . 60.60.12 / (60.12+60.60+60.12)/16 = 4 so expect about 4 TF/s on a GCD
/////////////////////////////////////////////////////////////////////////////////////
void synchronise(void)
{
#ifdef GRID_HIP
auto err = hipDeviceSynchronize();
assert(err==hipSuccess);
#endif
#ifdef GRID_CUDA
auto err = cudaDeviceSynchronize();
assert(err==cudaSuccess);
#endif
#ifdef GRID_SYCL
accelerator_barrier();
#endif
#ifdef GRID_ONE_MKL
gridblasHandle->wait();
#endif
}
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/////////////////////////////////////////////////////////////
// Single matrix GEMM -- fp64 and fp32
/////////////////////////////////////////////////////////////
void gemm(GridBLASOperation_t OpA,
GridBLASOperation_t OpB,
int m,int n, int k,
ComplexD alpha,
ComplexD* Amk, // Device pointer
ComplexD* Bkn,
ComplexD beta,
ComplexD* Cmn)
{
RealD t2=usecond();
assert(OpA!=GridBLAS_OP_T); // Complex case expect no transpose
assert(OpB!=GridBLAS_OP_T);
int lda = m; // m x k column major
int ldb = k; // k x n column major
int ldc = m; // m x b column major
if(OpA!=GridBLAS_OP_N)
lda = k;
if(OpB!=GridBLAS_OP_N)
ldb = n;
static deviceVector<ComplexD> alpha_p(1);
static deviceVector<ComplexD> beta_p(1);
// can prestore the 1 and the zero on device
acceleratorCopyToDevice((void *)&alpha,(void *)&alpha_p[0],sizeof(ComplexD));
acceleratorCopyToDevice((void *)&beta ,(void *)&beta_p[0],sizeof(ComplexD));
RealD t0=usecond();
#ifdef GRID_HIP
hipblasOperation_t hOpA;
hipblasOperation_t hOpB;
if ( OpA == GridBLAS_OP_N ) hOpA = HIPBLAS_OP_N;
if ( OpA == GridBLAS_OP_T ) hOpA = HIPBLAS_OP_T;
if ( OpA == GridBLAS_OP_C ) hOpA = HIPBLAS_OP_C;
if ( OpB == GridBLAS_OP_N ) hOpB = HIPBLAS_OP_N;
if ( OpB == GridBLAS_OP_T ) hOpB = HIPBLAS_OP_T;
if ( OpB == GridBLAS_OP_C ) hOpB = HIPBLAS_OP_C;
auto err = hipblasZgemm(gridblasHandle,
hOpA,
hOpB,
m,n,k,
(hipblasDoubleComplex *) &alpha_p[0],
(hipblasDoubleComplex *) Amk, lda,
(hipblasDoubleComplex *) Bkn, ldb,
(hipblasDoubleComplex *) &beta_p[0],
(hipblasDoubleComplex *) Cmn, ldc);
assert(err==HIPBLAS_STATUS_SUCCESS);
#endif
#ifdef GRID_CUDA
cublasOperation_t hOpA;
cublasOperation_t hOpB;
if ( OpA == GridBLAS_OP_N ) hOpA = CUBLAS_OP_N;
if ( OpA == GridBLAS_OP_T ) hOpA = CUBLAS_OP_T;
if ( OpA == GridBLAS_OP_C ) hOpA = CUBLAS_OP_C;
if ( OpB == GridBLAS_OP_N ) hOpB = CUBLAS_OP_N;
if ( OpB == GridBLAS_OP_T ) hOpB = CUBLAS_OP_T;
if ( OpB == GridBLAS_OP_C ) hOpB = CUBLAS_OP_C;
auto err = cublasZgemm(gridblasHandle,
hOpA,
hOpB,
m,n,k,
(cuDoubleComplex *) &alpha_p[0],
(cuDoubleComplex *) Amk, lda,
(cuDoubleComplex *) Bkn, ldb,
(cuDoubleComplex *) &beta_p[0],
(cuDoubleComplex *) Cmn, ldc);
assert(err==CUBLAS_STATUS_SUCCESS);
#endif
#ifdef GRID_SYCL
int64_t m64=m;
int64_t n64=n;
int64_t k64=k;
int64_t lda64=lda;
int64_t ldb64=ldb;
int64_t ldc64=ldc;
oneapi::mkl::transpose iOpA;
oneapi::mkl::transpose iOpB;
if ( OpA == GridBLAS_OP_N ) iOpA = oneapi::mkl::transpose::N;
if ( OpA == GridBLAS_OP_T ) iOpA = oneapi::mkl::transpose::T;
if ( OpA == GridBLAS_OP_C ) iOpA = oneapi::mkl::transpose::C;
if ( OpB == GridBLAS_OP_N ) iOpB = oneapi::mkl::transpose::N;
if ( OpB == GridBLAS_OP_T ) iOpB = oneapi::mkl::transpose::T;
if ( OpB == GridBLAS_OP_C ) iOpB = oneapi::mkl::transpose::C;
oneapi::mkl::blas::column_major::gemm(*gridblasHandle,
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iOpA,
iOpB,
m64,n64,k64,
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(ComplexD *) &alpha_p[0],
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(const ComplexD *)Amk, (int64_t )lda64,
(const ComplexD *)Bkn, (int64_t )ldb64,
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(ComplexD *) &beta_p[0],
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(ComplexD *)Cmn, (int64_t)ldc64);
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synchronise();
#endif
#if !defined(GRID_SYCL) && !defined(GRID_CUDA) && !defined(GRID_HIP)
// Need a default/reference implementation; use Eigen
if ( (OpA == GridBLAS_OP_N ) && (OpB == GridBLAS_OP_N) ) {
Eigen::Map<Eigen::MatrixXcd> eAmk(Amk,m,k);
Eigen::Map<Eigen::MatrixXcd> eBkn(Bkn,k,n);
Eigen::Map<Eigen::MatrixXcd> eCmn(Cmn,m,n);
eCmn = beta * eCmn + alpha * eAmk * eBkn ;
} else if ( (OpA == GridBLAS_OP_C ) && (OpB == GridBLAS_OP_N) ) {
Eigen::Map<Eigen::MatrixXcd> eAmk(Amk,k,m);
Eigen::Map<Eigen::MatrixXcd> eBkn(Bkn,k,n);
Eigen::Map<Eigen::MatrixXcd> eCmn(Cmn,m,n);
eCmn = beta * eCmn + alpha * eAmk.adjoint() * eBkn ;
} else if ( (OpA == GridBLAS_OP_N ) && (OpB == GridBLAS_OP_C) ) {
Eigen::Map<Eigen::MatrixXcd> eAmk(Amk,m,k);
Eigen::Map<Eigen::MatrixXcd> eBkn(Bkn,n,k);
Eigen::Map<Eigen::MatrixXcd> eCmn(Cmn,m,n);
eCmn = beta * eCmn + alpha * eAmk * eBkn.adjoint() ;
} else if ( (OpA == GridBLAS_OP_C ) && (OpB == GridBLAS_OP_C) ) {
Eigen::Map<Eigen::MatrixXcd> eAmk(Amk,k,m);
Eigen::Map<Eigen::MatrixXcd> eBkn(Bkn,n,k);
Eigen::Map<Eigen::MatrixXcd> eCmn(Cmn,m,n);
eCmn = beta * eCmn + alpha * eAmk.adjoint() * eBkn.adjoint() ;
} else {
assert(0);
}
#endif
RealD t1=usecond();
RealD flops = 8.0*m*n*k;
RealD bytes = 1.0*sizeof(ComplexD)*(m*k+k*n+m*n);
}
void gemm(GridBLASOperation_t OpA,
GridBLASOperation_t OpB,
int m,int n, int k,
ComplexF alpha,
ComplexF* Amk, // Device pointer
ComplexF* Bkn,
ComplexF beta,
ComplexF* Cmn)
{
RealD t2=usecond();
assert(OpA!=GridBLAS_OP_T); // Complex case expect no transpose
assert(OpB!=GridBLAS_OP_T);
int lda = m; // m x k column major
int ldb = k; // k x n column major
int ldc = m; // m x b column major
if(OpA!=GridBLAS_OP_N)
lda = k;
if(OpB!=GridBLAS_OP_N)
ldb = n;
static deviceVector<ComplexF> alpha_p(1);
static deviceVector<ComplexF> beta_p(1);
// can prestore the 1 and the zero on device
acceleratorCopyToDevice((void *)&alpha,(void *)&alpha_p[0],sizeof(ComplexF));
acceleratorCopyToDevice((void *)&beta ,(void *)&beta_p[0],sizeof(ComplexF));
RealD t0=usecond();
#ifdef GRID_HIP
hipblasOperation_t hOpA;
hipblasOperation_t hOpB;
if ( OpA == GridBLAS_OP_N ) hOpA = HIPBLAS_OP_N;
if ( OpA == GridBLAS_OP_T ) hOpA = HIPBLAS_OP_T;
if ( OpA == GridBLAS_OP_C ) hOpA = HIPBLAS_OP_C;
if ( OpB == GridBLAS_OP_N ) hOpB = HIPBLAS_OP_N;
if ( OpB == GridBLAS_OP_T ) hOpB = HIPBLAS_OP_T;
if ( OpB == GridBLAS_OP_C ) hOpB = HIPBLAS_OP_C;
auto err = hipblasCgemm(gridblasHandle,
hOpA,
hOpB,
m,n,k,
(hipblasComplex *) &alpha_p[0],
(hipblasComplex *) Amk, lda,
(hipblasComplex *) Bkn, ldb,
(hipblasComplex *) &beta_p[0],
(hipblasComplex *) Cmn, ldc);
assert(err==HIPBLAS_STATUS_SUCCESS);
#endif
#ifdef GRID_CUDA
cublasOperation_t hOpA;
cublasOperation_t hOpB;
if ( OpA == GridBLAS_OP_N ) hOpA = CUBLAS_OP_N;
if ( OpA == GridBLAS_OP_T ) hOpA = CUBLAS_OP_T;
if ( OpA == GridBLAS_OP_C ) hOpA = CUBLAS_OP_C;
if ( OpB == GridBLAS_OP_N ) hOpB = CUBLAS_OP_N;
if ( OpB == GridBLAS_OP_T ) hOpB = CUBLAS_OP_T;
if ( OpB == GridBLAS_OP_C ) hOpB = CUBLAS_OP_C;
auto err = cublasCgemm(gridblasHandle,
hOpA,
hOpB,
m,n,k,
(cuComplex *) &alpha_p[0],
(cuComplex *) Amk, lda,
(cuComplex *) Bkn, ldb,
(cuComplex *) &beta_p[0],
(cuComplex *) Cmn, ldc);
assert(err==CUBLAS_STATUS_SUCCESS);
#endif
#ifdef GRID_SYCL
int64_t m64=m;
int64_t n64=n;
int64_t k64=k;
int64_t lda64=lda;
int64_t ldb64=ldb;
int64_t ldc64=ldc;
oneapi::mkl::transpose iOpA;
oneapi::mkl::transpose iOpB;
if ( OpA == GridBLAS_OP_N ) iOpA = oneapi::mkl::transpose::N;
if ( OpA == GridBLAS_OP_T ) iOpA = oneapi::mkl::transpose::T;
if ( OpA == GridBLAS_OP_C ) iOpA = oneapi::mkl::transpose::C;
if ( OpB == GridBLAS_OP_N ) iOpB = oneapi::mkl::transpose::N;
if ( OpB == GridBLAS_OP_T ) iOpB = oneapi::mkl::transpose::T;
if ( OpB == GridBLAS_OP_C ) iOpB = oneapi::mkl::transpose::C;
oneapi::mkl::blas::column_major::gemm(*gridblasHandle,
Blas fix
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iOpA,
iOpB,
m64,n64,k64,
Improved the BLAS benchmark
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(ComplexF *) &alpha_p[0],
Blas fix
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(const ComplexF *)Amk, (int64_t )lda64,
(const ComplexF *)Bkn, (int64_t )ldb64,
Improved the BLAS benchmark
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(ComplexF *) &beta_p[0],
Blas fix
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(ComplexF *)Cmn, (int64_t )ldc64);
Improved the BLAS benchmark
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synchronise();
#endif
#if !defined(GRID_SYCL) && !defined(GRID_CUDA) && !defined(GRID_HIP)
// Need a default/reference implementation; use Eigen
if ( (OpA == GridBLAS_OP_N ) && (OpB == GridBLAS_OP_N) ) {
Eigen::Map<Eigen::MatrixXcf> eAmk(Amk,m,k);
Eigen::Map<Eigen::MatrixXcf> eBkn(Bkn,k,n);
Eigen::Map<Eigen::MatrixXcf> eCmn(Cmn,m,n);
eCmn = beta * eCmn + alpha * eAmk * eBkn ;
} else if ( (OpA == GridBLAS_OP_C ) && (OpB == GridBLAS_OP_N) ) {
Eigen::Map<Eigen::MatrixXcf> eAmk(Amk,k,m);
Eigen::Map<Eigen::MatrixXcf> eBkn(Bkn,k,n);
Eigen::Map<Eigen::MatrixXcf> eCmn(Cmn,m,n);
eCmn = beta * eCmn + alpha * eAmk.adjoint() * eBkn ;
} else if ( (OpA == GridBLAS_OP_N ) && (OpB == GridBLAS_OP_C) ) {
Eigen::Map<Eigen::MatrixXcf> eAmk(Amk,m,k);
Eigen::Map<Eigen::MatrixXcf> eBkn(Bkn,n,k);
Eigen::Map<Eigen::MatrixXcf> eCmn(Cmn,m,n);
eCmn = beta * eCmn + alpha * eAmk * eBkn.adjoint() ;
} else if ( (OpA == GridBLAS_OP_C ) && (OpB == GridBLAS_OP_C) ) {
Eigen::Map<Eigen::MatrixXcf> eAmk(Amk,k,m);
Eigen::Map<Eigen::MatrixXcf> eBkn(Bkn,n,k);
Eigen::Map<Eigen::MatrixXcf> eCmn(Cmn,m,n);
eCmn = beta * eCmn + alpha * eAmk.adjoint() * eBkn.adjoint() ;
} else {
assert(0);
}
#endif
RealD t1=usecond();
RealD flops = 8.0*m*n*k;
RealD bytes = 1.0*sizeof(ComplexF)*(m*k+k*n+m*n);
}
Blas benchmark committed stand alone
2024-08-20 17:18:43 +01:00
Improved the BLAS benchmark
2024-08-27 19:53:12 +01:00
/////////////////////////////////////////////////////////////
Blas benchmark committed stand alone
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void gemmBatched(int m,int n, int k,
ComplexD alpha,
deviceVector<ComplexD*> &Amk, // pointer list to matrices
deviceVector<ComplexD*> &Bkn,
ComplexD beta,
deviceVector<ComplexD*> &Cmn)
{
gemmBatched(GridBLAS_OP_N,GridBLAS_OP_N,
m,n,k,
alpha,
Amk,
Bkn,
beta,
Cmn);
}
void gemmBatched(int m,int n, int k,
ComplexF alpha,
deviceVector<ComplexF*> &Amk, // pointer list to matrices
deviceVector<ComplexF*> &Bkn,
ComplexF beta,
deviceVector<ComplexF*> &Cmn)
{
gemmBatched(GridBLAS_OP_N,GridBLAS_OP_N,
m,n,k,
alpha,
Amk,
Bkn,
beta,
Cmn);
}
void gemmBatched(GridBLASOperation_t OpA,
GridBLASOperation_t OpB,
int m,int n, int k,
ComplexD alpha,
deviceVector<ComplexD*> &Amk, // pointer list to matrices
deviceVector<ComplexD*> &Bkn,
ComplexD beta,
deviceVector<ComplexD*> &Cmn)
{
RealD t2=usecond();
int32_t batchCount = Amk.size();
assert(Bkn.size()==batchCount);
assert(Cmn.size()==batchCount);
assert(OpA!=GridBLAS_OP_T); // Complex case expect no transpose
assert(OpB!=GridBLAS_OP_T);
int lda = m; // m x k column major
int ldb = k; // k x n column major
int ldc = m; // m x b column major
if(OpA!=GridBLAS_OP_N)
lda = k;
if(OpB!=GridBLAS_OP_N)
ldb = n;
static deviceVector<ComplexD> alpha_p(1);
static deviceVector<ComplexD> beta_p(1);
// can prestore the 1 and the zero on device
acceleratorCopyToDevice((void *)&alpha,(void *)&alpha_p[0],sizeof(ComplexD));
acceleratorCopyToDevice((void *)&beta ,(void *)&beta_p[0],sizeof(ComplexD));
RealD t0=usecond();
// std::cout << "ZgemmBatched mnk "<<m<<","<<n<<","<<k<<" count "<<batchCount<<std::endl;
#ifdef GRID_HIP
hipblasOperation_t hOpA;
hipblasOperation_t hOpB;
if ( OpA == GridBLAS_OP_N ) hOpA = HIPBLAS_OP_N;
if ( OpA == GridBLAS_OP_T ) hOpA = HIPBLAS_OP_T;
if ( OpA == GridBLAS_OP_C ) hOpA = HIPBLAS_OP_C;
if ( OpB == GridBLAS_OP_N ) hOpB = HIPBLAS_OP_N;
if ( OpB == GridBLAS_OP_T ) hOpB = HIPBLAS_OP_T;
if ( OpB == GridBLAS_OP_C ) hOpB = HIPBLAS_OP_C;
auto err = hipblasZgemmBatched(gridblasHandle,
hOpA,
hOpB,
m,n,k,
(hipblasDoubleComplex *) &alpha_p[0],
(hipblasDoubleComplex **)&Amk[0], lda,
(hipblasDoubleComplex **)&Bkn[0], ldb,
(hipblasDoubleComplex *) &beta_p[0],
(hipblasDoubleComplex **)&Cmn[0], ldc,
batchCount);
// std::cout << " hipblas return code " <<(int)err<<std::endl;
assert(err==HIPBLAS_STATUS_SUCCESS);
#endif
#ifdef GRID_CUDA
cublasOperation_t hOpA;
cublasOperation_t hOpB;
if ( OpA == GridBLAS_OP_N ) hOpA = CUBLAS_OP_N;
if ( OpA == GridBLAS_OP_T ) hOpA = CUBLAS_OP_T;
if ( OpA == GridBLAS_OP_C ) hOpA = CUBLAS_OP_C;
if ( OpB == GridBLAS_OP_N ) hOpB = CUBLAS_OP_N;
if ( OpB == GridBLAS_OP_T ) hOpB = CUBLAS_OP_T;
if ( OpB == GridBLAS_OP_C ) hOpB = CUBLAS_OP_C;
auto err = cublasZgemmBatched(gridblasHandle,
hOpA,
hOpB,
m,n,k,
(cuDoubleComplex *) &alpha_p[0],
(cuDoubleComplex **)&Amk[0], lda,
(cuDoubleComplex **)&Bkn[0], ldb,
(cuDoubleComplex *) &beta_p[0],
(cuDoubleComplex **)&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 lda64=lda;
int64_t ldb64=ldb;
int64_t ldc64=ldc;
int64_t batchCount64=batchCount;
oneapi::mkl::transpose iOpA;
oneapi::mkl::transpose iOpB;
if ( OpA == GridBLAS_OP_N ) iOpA = oneapi::mkl::transpose::N;
if ( OpA == GridBLAS_OP_T ) iOpA = oneapi::mkl::transpose::T;
if ( OpA == GridBLAS_OP_C ) iOpA = oneapi::mkl::transpose::C;
if ( OpB == GridBLAS_OP_N ) iOpB = oneapi::mkl::transpose::N;
if ( OpB == GridBLAS_OP_T ) iOpB = oneapi::mkl::transpose::T;
if ( OpB == GridBLAS_OP_C ) iOpB = oneapi::mkl::transpose::C;
oneapi::mkl::blas::column_major::gemm_batch(*gridblasHandle,
&iOpA,
&iOpB,
&m64,&n64,&k64,
(ComplexD *) &alpha_p[0],
(const ComplexD **)&Amk[0], (const int64_t *)&lda64,
(const ComplexD **)&Bkn[0], (const int64_t *)&ldb64,
(ComplexD *) &beta_p[0],
(ComplexD **)&Cmn[0], (const int64_t *)&ldc64,
(int64_t)1,&batchCount64,std::vector<sycl::event>());
synchronise();
#if 0
// This code was used to check the mat mul on Sunspot/OneMKL
std::cerr << " Called SYCL batched ZGEMM OpA "<< OpA << " OpB "<<OpB <<std::endl;
std::vector<ComplexD> A(m*k); // pointer list to matrices
std::vector<ComplexD> B(k*n);
std::vector<ComplexD> C(m*n);
// int sda = lda*k;
// int sdb = ldb*k;
// int sdc = ldc*n;
std::cerr << " Checking the GEMM results "<<std::endl;
for (int p = 0; p < 1; ++p) {
ComplexD * Amk_p; // pointer list to matrices
ComplexD * Bkn_p; // pointer list to matrices
ComplexD * Cmn_p; // pointer list to matrices
acceleratorCopyFromDevice((void *)&Amk[p],(void *)&Amk_p,sizeof(ComplexD*));
acceleratorCopyFromDevice((void *)&Bkn[p],(void *)&Bkn_p,sizeof(ComplexD*));
acceleratorCopyFromDevice((void *)&Cmn[p],(void *)&Cmn_p,sizeof(ComplexD*));
std::cerr << " p " << p << " copied pointers "<<std::endl;
acceleratorCopyFromDevice((void *)Amk_p,(void *)&A[0],m*k*sizeof(ComplexD));
acceleratorCopyFromDevice((void *)Bkn_p,(void *)&B[0],k*n*sizeof(ComplexD));
acceleratorCopyFromDevice((void *)Cmn_p,(void *)&C[0],m*n*sizeof(ComplexD));
std::cerr << " p " << p << " copied matrices "<<std::endl;
std::cerr << " C[0] "<<C[0]<<std::endl;
std::cerr << " A[0] "<<A[0]<<std::endl;
std::cerr << " B[0] "<<B[0]<<std::endl;
std::cerr << " m "<<m<<std::endl;
std::cerr << " n "<<n<<std::endl;
std::cerr << " k "<<k<<std::endl;
for (int mm = 0; mm < m; ++mm) {
for (int nn = 0; nn < n; ++nn) {
ComplexD c_mn(0.0);
for (int kk = 0; kk < k; ++kk) {
int idx_a, idx_b;
// int lda = m; // m x k column major
// int ldb = k; // k x n column major
// int ldc = m; // m x b column major
if(OpA!=GridBLAS_OP_N) {
idx_a =kk + mm*lda;
} else {
idx_a =mm + kk*lda;
}
if(OpB!=GridBLAS_OP_N) {
idx_b =nn + kk*ldb;
} else {
idx_b =kk + nn*ldb;
}
// std::cerr << " idx_a "<<idx_a<<" idx_b "<<idx_b<<std::endl;
ComplexD Ac = A[idx_a];
ComplexD Bc = B[idx_b];
if(OpA==GridBLAS_OP_C) Ac = conjugate(Ac);
if(OpB==GridBLAS_OP_C) Bc = conjugate(Bc);
c_mn += Ac*Bc;
}
std::cerr << " beta "<<beta<<" alpha "<<alpha<<" C_"<<mm<<","<<nn<<" "<<c_mn<<" "<<C[mm + nn*ldc]<<std::endl;
}
}
}
#endif
#endif
#if !defined(GRID_SYCL) && !defined(GRID_CUDA) && !defined(GRID_HIP)
// Need a default/reference implementation; use Eigen
if ( (OpA == GridBLAS_OP_N ) && (OpB == GridBLAS_OP_N) ) {
thread_for (p, batchCount, {
Eigen::Map<Eigen::MatrixXcd> eAmk(Amk[p],m,k);
Eigen::Map<Eigen::MatrixXcd> eBkn(Bkn[p],k,n);
Eigen::Map<Eigen::MatrixXcd> eCmn(Cmn[p],m,n);
eCmn = beta * eCmn + alpha * eAmk * eBkn ;
});
} else if ( (OpA == GridBLAS_OP_C ) && (OpB == GridBLAS_OP_N) ) {
thread_for (p, batchCount, {
Eigen::Map<Eigen::MatrixXcd> eAmk(Amk[p],k,m);
Eigen::Map<Eigen::MatrixXcd> eBkn(Bkn[p],k,n);
Eigen::Map<Eigen::MatrixXcd> eCmn(Cmn[p],m,n);
eCmn = beta * eCmn + alpha * eAmk.adjoint() * eBkn ;
});
} else if ( (OpA == GridBLAS_OP_N ) && (OpB == GridBLAS_OP_C) ) {
thread_for (p, batchCount, {
Eigen::Map<Eigen::MatrixXcd> eAmk(Amk[p],m,k);
Eigen::Map<Eigen::MatrixXcd> eBkn(Bkn[p],n,k);
Eigen::Map<Eigen::MatrixXcd> eCmn(Cmn[p],m,n);
eCmn = beta * eCmn + alpha * eAmk * eBkn.adjoint() ;
});
} else if ( (OpA == GridBLAS_OP_C ) && (OpB == GridBLAS_OP_C) ) {
thread_for (p, batchCount, {
Eigen::Map<Eigen::MatrixXcd> eAmk(Amk[p],k,m);
Eigen::Map<Eigen::MatrixXcd> eBkn(Bkn[p],n,k);
Eigen::Map<Eigen::MatrixXcd> eCmn(Cmn[p],m,n);
eCmn = beta * eCmn + alpha * eAmk.adjoint() * eBkn.adjoint() ;
} );
} else {
assert(0);
}
#endif
RealD t1=usecond();
RealD flops = 8.0*m*n*k*batchCount;
RealD bytes = 1.0*sizeof(ComplexD)*(m*k+k*n+m*n)*batchCount;
// std::cout <<GridLogMessage<< " batched Blas copy "<<(t0-t2)/1.e3 <<" ms "<<std::endl;
// std::cout <<GridLogMessage<< " batched Blas zGemm call "<<m<<","<<n<<","<<k<<" "<< flops/(t1-t0)/1.e3 <<" GF/s "<<(t1-t0)/1.e3<<" ms "<<std::endl;
// std::cout <<GridLogMessage<< " batched Blas zGemm call "<<m<<","<<n<<","<<k<<" "<< bytes/(t1-t0)/1.e3 <<" GB/s "<<(t1-t0)/1.e3<<" ms "<<std::endl;
}
void gemmBatched(GridBLASOperation_t OpA,
GridBLASOperation_t OpB,
int m,int n, int k,
ComplexF alpha,
deviceVector<ComplexF*> &Amk, // pointer list to matrices
deviceVector<ComplexF*> &Bkn,
ComplexF beta,
deviceVector<ComplexF*> &Cmn)
{
RealD t2=usecond();
int32_t batchCount = Amk.size();
assert(OpA!=GridBLAS_OP_T); // Complex case expect no transpose
assert(OpB!=GridBLAS_OP_T);
int lda = m; // m x k column major
int ldb = k; // k x n column major
int ldc = m; // m x b column major
if(OpA!=GridBLAS_OP_N)
lda = k;
if(OpB!=GridBLAS_OP_N)
ldb = n;
static deviceVector<ComplexF> alpha_p(1);
static deviceVector<ComplexF> beta_p(1);
// can prestore the 1 and the zero on device
acceleratorCopyToDevice((void *)&alpha,(void *)&alpha_p[0],sizeof(ComplexF));
acceleratorCopyToDevice((void *)&beta ,(void *)&beta_p[0],sizeof(ComplexF));
RealD t0=usecond();
assert(Bkn.size()==batchCount);
assert(Cmn.size()==batchCount);
#ifdef GRID_HIP
hipblasOperation_t hOpA;
hipblasOperation_t hOpB;
if ( OpA == GridBLAS_OP_N ) hOpA = HIPBLAS_OP_N;
if ( OpA == GridBLAS_OP_T ) hOpA = HIPBLAS_OP_T;
if ( OpA == GridBLAS_OP_C ) hOpA = HIPBLAS_OP_C;
if ( OpB == GridBLAS_OP_N ) hOpB = HIPBLAS_OP_N;
if ( OpB == GridBLAS_OP_T ) hOpB = HIPBLAS_OP_T;
if ( OpB == GridBLAS_OP_C ) hOpB = HIPBLAS_OP_C;
auto err = hipblasCgemmBatched(gridblasHandle,
hOpA,
hOpB,
m,n,k,
(hipblasComplex *) &alpha_p[0],
(hipblasComplex **)&Amk[0], lda,
(hipblasComplex **)&Bkn[0], ldb,
(hipblasComplex *) &beta_p[0],
(hipblasComplex **)&Cmn[0], ldc,
batchCount);
assert(err==HIPBLAS_STATUS_SUCCESS);
#endif
#ifdef GRID_CUDA
cublasOperation_t hOpA;
cublasOperation_t hOpB;
if ( OpA == GridBLAS_OP_N ) hOpA = CUBLAS_OP_N;
if ( OpA == GridBLAS_OP_T ) hOpA = CUBLAS_OP_T;
if ( OpA == GridBLAS_OP_C ) hOpA = CUBLAS_OP_C;
if ( OpB == GridBLAS_OP_N ) hOpB = CUBLAS_OP_N;
if ( OpB == GridBLAS_OP_T ) hOpB = CUBLAS_OP_T;
if ( OpB == GridBLAS_OP_C ) hOpB = CUBLAS_OP_C;
auto err = cublasCgemmBatched(gridblasHandle,
hOpA,
hOpB,
m,n,k,
(cuComplex *) &alpha_p[0],
(cuComplex **)&Amk[0], lda,
(cuComplex **)&Bkn[0], ldb,
(cuComplex *) &beta_p[0],
(cuComplex **)&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 lda64=lda;
int64_t ldb64=ldb;
int64_t ldc64=ldc;
int64_t batchCount64=batchCount;
oneapi::mkl::transpose iOpA;
oneapi::mkl::transpose iOpB;
if ( OpA == GridBLAS_OP_N ) iOpA = oneapi::mkl::transpose::N;
if ( OpA == GridBLAS_OP_T ) iOpA = oneapi::mkl::transpose::T;
if ( OpA == GridBLAS_OP_C ) iOpA = oneapi::mkl::transpose::C;
if ( OpB == GridBLAS_OP_N ) iOpB = oneapi::mkl::transpose::N;
if ( OpB == GridBLAS_OP_T ) iOpB = oneapi::mkl::transpose::T;
if ( OpB == GridBLAS_OP_C ) iOpB = oneapi::mkl::transpose::C;
oneapi::mkl::blas::column_major::gemm_batch(*gridblasHandle,
&iOpA,
&iOpB,
&m64,&n64,&k64,
(ComplexF *) &alpha_p[0],
(const ComplexF **)&Amk[0], (const int64_t *)&lda64,
(const ComplexF **)&Bkn[0], (const int64_t *)&ldb64,
(ComplexF *) &beta_p[0],
(ComplexF **)&Cmn[0], (const int64_t *)&ldc64,
(int64_t)1,&batchCount64,std::vector<sycl::event>());
synchronise();
#endif
#if !defined(GRID_SYCL) && !defined(GRID_CUDA) && !defined(GRID_HIP)
// Need a default/reference implementation; use Eigen
if ( (OpA == GridBLAS_OP_N ) && (OpB == GridBLAS_OP_N) ) {
thread_for (p, batchCount, {
Eigen::Map<Eigen::MatrixXcf> eAmk(Amk[p],m,k);
Eigen::Map<Eigen::MatrixXcf> eBkn(Bkn[p],k,n);
Eigen::Map<Eigen::MatrixXcf> eCmn(Cmn[p],m,n);
eCmn = beta * eCmn + alpha * eAmk * eBkn ;
});
} else if ( (OpA == GridBLAS_OP_C ) && (OpB == GridBLAS_OP_N) ) {
thread_for (p, batchCount, {
Eigen::Map<Eigen::MatrixXcf> eAmk(Amk[p],k,m);
Eigen::Map<Eigen::MatrixXcf> eBkn(Bkn[p],k,n);
Eigen::Map<Eigen::MatrixXcf> eCmn(Cmn[p],m,n);
eCmn = beta * eCmn + alpha * eAmk.adjoint() * eBkn ;
});
} else if ( (OpA == GridBLAS_OP_N ) && (OpB == GridBLAS_OP_C) ) {
thread_for (p, batchCount, {
Eigen::Map<Eigen::MatrixXcf> eAmk(Amk[p],m,k);
Eigen::Map<Eigen::MatrixXcf> eBkn(Bkn[p],n,k);
Eigen::Map<Eigen::MatrixXcf> eCmn(Cmn[p],m,n);
eCmn = beta * eCmn + alpha * eAmk * eBkn.adjoint() ;
});
} else if ( (OpA == GridBLAS_OP_C ) && (OpB == GridBLAS_OP_C) ) {
thread_for (p, batchCount, {
Eigen::Map<Eigen::MatrixXcf> eAmk(Amk[p],k,m);
Eigen::Map<Eigen::MatrixXcf> eBkn(Bkn[p],n,k);
Eigen::Map<Eigen::MatrixXcf> eCmn(Cmn[p],m,n);
eCmn = beta * eCmn + alpha * eAmk.adjoint() * eBkn.adjoint() ;
} );
} else {
assert(0);
}
#endif
RealD t1=usecond();
RealD flops = 8.0*m*n*k*batchCount;
RealD bytes = 1.0*sizeof(ComplexF)*(m*k+k*n+m*n)*batchCount;
}
template<class CComplex>
double benchmark(int M, int N, int K, int BATCH)
{
int32_t N_A = M*K*BATCH;
int32_t N_B = K*N*BATCH;
int32_t N_C = M*N*BATCH;
deviceVector<CComplex> A(N_A); acceleratorMemSet(&A[0],0,N_A*sizeof(CComplex));
deviceVector<CComplex> B(N_B); acceleratorMemSet(&B[0],0,N_B*sizeof(CComplex));
deviceVector<CComplex> C(N_C); acceleratorMemSet(&C[0],0,N_C*sizeof(CComplex));
CComplex alpha(1.0);
CComplex beta (1.0);
RealD flops = 8.0*M*N*K*BATCH;
int ncall=10;
deviceVector<CComplex *> As(BATCH);
deviceVector<CComplex *> Bs(BATCH);
deviceVector<CComplex *> Cs(BATCH);
for(int b = 0 ; b < BATCH;b++) {
CComplex *ptr;
ptr = &A[b*M*K]; acceleratorPut(As[b],ptr);
ptr = &B[b*K*N]; acceleratorPut(Bs[b],ptr);
ptr = &C[b*M*N]; acceleratorPut(Cs[b],ptr);
}
gemmBatched(M,N,K,
alpha,
As, // m x k
Bs, // k x n
beta,
Cs);
synchronise();
RealD t0 = usecond();
for(int i=0;i<ncall;i++){
gemmBatched(M,N,K,
alpha,
As, // m x k
Bs, // k x n
beta,
Cs);
synchronise();
}
RealD t1 = usecond();
RealD bytes = 1.0*sizeof(CComplex)*(M*N*2+N*K+M*K)*BATCH;
flops = 8.0*M*N*K*BATCH*ncall;
flops = flops/(t1-t0)/1.e3;
return flops; // Returns gigaflops
}
Improved the BLAS benchmark
2024-08-27 19:53:12 +01:00
template<class CComplex>
double benchmark(int M, int N, int K)
{
int32_t N_A = M*K;
int32_t N_B = K*N;
int32_t N_C = M*N;
deviceVector<CComplex> A(N_A); acceleratorMemSet(&A[0],0,N_A*sizeof(CComplex));
deviceVector<CComplex> B(N_B); acceleratorMemSet(&B[0],0,N_B*sizeof(CComplex));
deviceVector<CComplex> C(N_C); acceleratorMemSet(&C[0],0,N_C*sizeof(CComplex));
CComplex alpha(1.0);
CComplex beta (1.0);
RealD flops = 8.0*M*N*K;
int ncall=10;
gemm(GridBLAS_OP_C,GridBLAS_OP_N,
M,N,K,
alpha,
&A[0], // m x k
&B[0], // k x n
beta,
&C[0]);
synchronise();
RealD t0 = usecond();
for(int i=0;i<ncall;i++){
gemm(GridBLAS_OP_N,GridBLAS_OP_N,
M,N,K,
alpha,
&A[0], // m x k
&B[0], // k x n
beta,
&C[0]);
synchronise();
}
RealD t1 = usecond();
RealD bytes = 1.0*sizeof(CComplex)*(M*N*2+N*K+M*K);
flops = 8.0*M*N*K*ncall;
flops = flops/(t1-t0)/1.e3;
return flops; // Returns gigaflops
}
Blas benchmark committed stand alone
2024-08-20 17:18:43 +01:00
};
gridblasHandle_t GridBLAS::gridblasHandle;
int GridBLAS::gridblasInit;
FILE * FP;
template<class CComplex>
static void BLAS(void)
{
//int nbasis, int nrhs, int coarseVol
int basis[] = { 16,32,64 };
int rhs[] = { 8,12,16 };
int vol = 8*8*8*8;
int blk = 4*4*4*4;
GridBLAS blas;
int fpbits = sizeof(CComplex)*4;
std::cout<< "=================================================================================="<<std::endl;
std::cout<< "= batched GEMM fp"<<fpbits<<std::endl;
std::cout<< "=================================================================================="<<std::endl;
std::cout << " M "<<"\t\t"<<"N"<<"\t\t\t"<<"K"<<"\t\t"<<"Gflop/s / rank (coarse mrhs)"<<std::endl;
std::cout << "----------------------------------------------------------"<<std::endl;
fprintf(FP,"GEMM\n\n M, N, K, BATCH, GF/s per rank fp%d\n",fpbits);
for(int b=0;b<3;b++){
for(int r=0;r<3;r++){
int M=basis[b];
int N=rhs[r];
int K=basis[b];
int BATCH=vol;
double p=blas.benchmark<CComplex>(M,N,K,BATCH);
fprintf(FP,"%d, %d, %d, %d, %f\n", M, N, K, BATCH, p);
std::cout<< M<<"\t\t"<<N<<"\t\t"<<K<<"\t\t"<<BATCH<<"\t\t"<<p<<std::endl;
}}
std::cout << "----------------------------------------------------------"<<std::endl;
std::cout << " M "<<"\t\t"<<"N"<<"\t\t\t"<<"K"<<"\t\t"<<"Gflop/s / rank (block project)"<<std::endl;
std::cout << "----------------------------------------------------------"<<std::endl;
for(int b=0;b<3;b++){
for(int r=0;r<3;r++){
int M=basis[b];
int N=rhs[r];
int K=blk;
int BATCH=vol;
double p=blas.benchmark<CComplex>(M,N,K,BATCH);
fprintf(FP,"%d, %d, %d, %d, %f\n", M, N, K, BATCH, p);
std::cout<< M<<"\t\t"<<N<<"\t\t"<<K<<"\t\t"<<BATCH<<"\t\t"<<p<<std::endl;
}}
std::cout << "----------------------------------------------------------"<<std::endl;
std::cout << " M "<<"\t\t"<<"N"<<"\t\t\t"<<"K"<<"\t\t"<<"Gflop/s / rank (block promote)"<<std::endl;
std::cout << "----------------------------------------------------------"<<std::endl;
for(int b=0;b<3;b++){
for(int r=0;r<3;r++){
int M=rhs[r];
int N=blk;
int K=basis[b];
int BATCH=vol;
double p=blas.benchmark<CComplex>(M,N,K,BATCH);
fprintf(FP,"%d, %d, %d, %d, %f\n", M, N, K, BATCH, p);
std::cout<< M<<"\t\t"<<N<<"\t\t"<<K<<"\t\t"<<BATCH<<"\t\t"<<p<<std::endl;
}}
fprintf(FP,"\n\n\n");
Improved the BLAS benchmark
2024-08-27 19:53:12 +01:00
std::cout << "----------------------------------------------------------"<<std::endl;
std::cout << " M "<<"\t\t"<<"N"<<"\t\t\t"<<"K"<<"\t\t"<<"Gflop/s / rank (inner product matrix)"<<std::endl;
std::cout << "----------------------------------------------------------"<<std::endl;
{
int M=12;
int N=12;
std::vector<int> ks({4*1024*1024, 2*1024*1024, 1024*1024, 256*1024, 1024 });
for( int kk=0;kk<ks.size();kk++ ) {
int K = ks[kk];
double p=blas.benchmark<CComplex>(M,N,K);
fprintf(FP,"%d, %d, %d, %d, %f\n", M, N, K, 1, p);
std::cout<< M<<"\t\t"<<N<<"\t\t"<<K<<"\t\t"<<1<<"\t\t"<<p<<std::endl;
}
}
Blas benchmark committed stand alone
2024-08-20 17:18:43 +01:00
std::cout << "=================================================================================="<<std::endl;
};
int main (int argc, char ** argv)
{
acceleratorInit();
FP = fopen("Benchmark_usqcd.csv","w");
std::cout << "=================================================================================="<<std::endl;
std::cout << " Batched BLAS benchmark " <<std::endl;
std::cout << "=================================================================================="<<std::endl;
BLAS<ComplexD>();
BLAS<ComplexF>();
fclose(FP);
}
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