CUDA: various improvements
This commit is contained in:
Philippe Tillet
@@ -94,7 +94,7 @@ void bench(sc::numeric_type dtype, std::string operation)
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cudaEventCreate(&stop);\
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OP;\
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cudaThreadSynchronize();\
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while(total_time*1e-3 < 1e-3){\
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while(total_time*1e-3 < 1e-2){\
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flush = sc::zeros(1e6, 1, dtype);\
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cudaEventRecord(start,0);\
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OP;\
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@@ -191,7 +191,6 @@ void bench(sc::numeric_type dtype, std::string operation)
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#endif
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#ifdef BENCH_CUBLAS
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T *cux, *cuy;
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T result;
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cudaMalloc((void**) &cux, N * sizeof(T));
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cudaMalloc((void**) &cuy, N * sizeof(T));
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BENCHMARK_CUDA(cublasSdot(N, cux, 1, cuy, 1), 2*N*dtsize/t)
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@@ -210,6 +209,7 @@ void bench(sc::numeric_type dtype, std::string operation)
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//AlexNet
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MNs.push_back(std::make_tuple('N',1000,256));
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MNs.push_back(std::make_tuple('N',4096,256));
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MNs.push_back(std::make_tuple('T',169,256));
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MNs.push_back(std::make_tuple('T',169,384));
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MNs.push_back(std::make_tuple('T',729,256));
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@@ -261,39 +261,43 @@ void bench(sc::numeric_type dtype, std::string operation)
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if(operation.substr(0,4)=="gemm")
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{
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std::vector<std::tuple<std::string, char, char, int_t, int_t, int_t> > MNKs;
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MNKs.push_back(std::make_tuple("Square [512]",'N','T',512,512,512));
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MNKs.push_back(std::make_tuple("Square [1536]",'N','T',1536,1536,1536));
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//AlexNet (Forward)
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MNKs.push_back(std::make_tuple("F-Conv1",'N','N',3025,96,363));
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MNKs.push_back(std::make_tuple("F-Conv2",'N','N',729,128,1200));
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MNKs.push_back(std::make_tuple("F-Conv3",'N','N',169,384,2304));
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MNKs.push_back(std::make_tuple("F-Conv4",'N','N',169,192,1728));
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MNKs.push_back(std::make_tuple("F-Conv5",'N','N',169,128,1728));
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//LeNet (Forward)
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MNKs.push_back(std::make_tuple("F-Conv1",'N','N',576,20,25));
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MNKs.push_back(std::make_tuple("F-Conv2",'N','N',64,50,500));
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//Square
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MNKs.push_back(std::make_tuple("Square [N=896]",'N','T',896,896,896));
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MNKs.push_back(std::make_tuple("Square [N=2560]",'N','T',2560,2560,2560));
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//AlexNet (Backward)
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MNKs.push_back(std::make_tuple("B-Conv5",'T','N',1728,128,169));
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MNKs.push_back(std::make_tuple("B-Conv4",'T','N',1728,192,169));
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MNKs.push_back(std::make_tuple("B-Conv3",'T','N',2304,384,169));
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MNKs.push_back(std::make_tuple("B-Conv2",'T','N',1200,128,729));
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MNKs.push_back(std::make_tuple("B-Conv1",'T','N',363,96,3025));
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//LeNet (Backward)
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MNKs.push_back(std::make_tuple("B-Conv2",'T','N',500,50,64));
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MNKs.push_back(std::make_tuple("B-Conv1",'T','N',25,20,576));
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//Convolution
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MNKs.push_back(std::make_tuple("Convolution [AlexNet-1]",'N','N',3025,96,363));
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MNKs.push_back(std::make_tuple("Convolution [AlexNet-2]",'N','N',729,128,1200));
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MNKs.push_back(std::make_tuple("Convolution [AlexNet-3]",'N','N',169,384,2304));
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MNKs.push_back(std::make_tuple("Convolution [AlexNet-4]",'N','N',169,192,1728));
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MNKs.push_back(std::make_tuple("Convolution [AlexNet-5]",'N','N',169,128,1728));
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// MNKs.push_back(std::make_tuple("Convolution [LeNet-1],'N','N',576,20,25));
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// MNKs.push_back(std::make_tuple("Convolution [LeNet-2]",'N','N',64,50,500));
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//Convolution Gradient-1
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MNKs.push_back(std::make_tuple("Convolution Gradient-1 [AlexNet-5]",'T','N',1728,128,169));
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MNKs.push_back(std::make_tuple("Convolution Gradient-1 [AlexNet-4]",'T','N',1728,192,169));
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MNKs.push_back(std::make_tuple("Convolution Gradient-1 [AlexNet-3]",'T','N',2304,384,169));
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MNKs.push_back(std::make_tuple("Convolution Gradient-1 [AlexNet-2]",'T','N',1200,128,729));
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MNKs.push_back(std::make_tuple("Convolution Gradient-1 [AlexNet-1]",'T','N',363,96,3025));
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// MNKs.push_back(std::make_tuple("Conv. Gradient-1 [LeNet-2]",'T','N',500,50,64));
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// MNKs.push_back(std::make_tuple("Conv. Gradient-1 [LeNet-1]",'T','N',25,20,576));
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MNKs.push_back(std::make_tuple("B-Conv5 [bottom]",'N','T',169,1728,128));
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MNKs.push_back(std::make_tuple("B-Conv4 [bottom]",'N','T',169,1728,192));
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MNKs.push_back(std::make_tuple("B-Conv3 [bottom]",'N','T',169,2304,384));
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MNKs.push_back(std::make_tuple("B-Conv2 [bottom]",'N','T',729,1200,128));
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//LeNet (Backward)
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MNKs.push_back(std::make_tuple("B-Conv2 [bottom]",'N','T',64,500,50));
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//Convolution Gradient-2
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MNKs.push_back(std::make_tuple("Convolution Gradient-2 [AlexNet-5]",'N','T',169,1728,128));
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MNKs.push_back(std::make_tuple("Convolution Gradient-2 [AlexNet-4]",'N','T',169,1728,192));
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MNKs.push_back(std::make_tuple("Convolution Gradient-2 [AlexNet-3]",'N','T',169,2304,384));
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MNKs.push_back(std::make_tuple("Convolution Gradient-2 [AlexNet-2]",'N','T',729,1200,128));
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// MNKs.push_back(std::make_tuple("Conv. Gradient-2 [LeNet-2]",'N','T',64,500,50));
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//Covariance (e.g., ICA)
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MNKs.push_back(std::make_tuple("ICA [32 channels]",'N','N',32,32,32000));
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MNKs.push_back(std::make_tuple("ICA [256 channels]",'N','N',256,256,32000));
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//Covariance (e.g., ICA, 10minutes/1khz)
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MNKs.push_back(std::make_tuple("ICA [32 channels]",'N','T',32,32,600000));
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MNKs.push_back(std::make_tuple("ICA [256 channels]",'N','T',256,256,600000));
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//Bi-diagonalization
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MNKs.push_back(std::make_tuple("Bidiagonalization [Iteration 1]",'N','T',4096,4096,32));
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MNKs.push_back(std::make_tuple("Bidiagonalization [Iteration 10]",'N','T',3456,3456,32));
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MNKs.push_back(std::make_tuple("Bidiagonalization [Iteration 50]",'N','T',896,896,32));
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/*---------*/
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/*--BLAS3--*/
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@@ -317,7 +321,7 @@ void bench(sc::numeric_type dtype, std::string operation)
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#ifdef HAS_A_BLAS
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int_t lda = A.ld(), ldb = B.ld(), ldc = C.ld();
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#endif
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BENCHMARK_ISAAC(C = sc::control(AT?(BT?dot(A.T(),B.T()):dot(A.T(),B)):(BT?dot(A,B.T()):dot(A,B)), sc::execution_options_type(0, &events)), (double)2*M*N*K/t);
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BENCHMARK_ISAAC(C = sc::control(AT?(BT?dot(A.T(),B.T()):dot(A.T(),B)):(BT?dot(A,B.T()):dot(A,B)), sc::execution_options_type(0, &events), sc::dispatcher_options_type(false)), (double)2*M*N*K/t);
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/* clblas */
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#ifdef BENCH_CLBLAS
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BENCHMARK_CLBLAS(clblasSgemm(clblasColumnMajor, AT?clblasTrans:clblasNoTrans, BT?clblasTrans:clblasNoTrans, M, N, K, 1, CL_HANDLE(A.data()), 0, lda, CL_HANDLE(B.data()), 0, ldb,
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@@ -336,7 +340,7 @@ void bench(sc::numeric_type dtype, std::string operation)
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cudaMalloc((void**) &cuA, M * K * sizeof(T));
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cudaMalloc((void**) &cuB, K * N * sizeof(T));
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cudaMalloc((void**) &cuC, M * N * sizeof(T));
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BENCHMARK_CUDA(cublasSgemm('n', 't', M, N, K, 1, cuA, lda, cuB, ldb, 1, cuC, ldc), (double)2*M*N*K/t)
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BENCHMARK_CUDA(cublasSgemm(AT?'t':'n', BT?'t':'n', M, N, K, 1, cuA, lda, cuB, ldb, 1, cuC, ldc), (double)2*M*N*K/t)
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cudaFree(cuA);
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cudaFree(cuB);
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cudaFree(cuC);
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