In particular, in the above example we could create 1024 CUDA™ streams using the function cudaStreamCreate(), then preface each call to cublas<t>gemm() with a call to cublasSetStream() with a different stream for each of the matrix-matrix multiplications (note that cublasSetStream() res...
void cinn_call_cublas(void *v_args, int num_args, bool trans_a, bool trans_b, bool trans_o, float alpha, float beta, int a1, int a2, int a3, int a4, int b1, int b2, int b3, int b4, void *stream) { // 省略 CUBLAS_CALL(cublasGemmStridedBatched(cuda_dtype, cuhandle, tra...
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Note: The non-deterministic behavior of multi-stream execution is due to library optimizations in selecting internal workspace for the routines running in parallel streams. To avoid this effect user can either: ‣ provide a separate workspace for each used stream using the cublasSetWorkspace() ...
generally between 64x64 and 256x256, which is not enough to run efficiently on GPU in sequential mode. Using streams parallelism, I'have managed to make the GPU competitive against a multicore CPU. I will check out Magma's functions to see if there are doing better than CUDA streamed ...
Back to the topic, literally, cublasGemmGroupedBatchedEx just need a device-side batch_sizes, and it doesn't need multi-stream, so that it can eliminate both the synchronization between cuda streams and the synchronization between host and device. My concern is that, for the grouped gemm ...
If you don’t like some of the trappings that come along with these design choices, you can, to some degree, opt-out of them, and revert to manual control, for example using non-UM methods, restricting UM activity to one device, or, for some use cases, via stream association methods:...
sockstreambuf.cpp sparse_vector.cpp stack.cpp static_map.cpp static_set.cpp statistics.cpp std_vector_c.cpp stft_good_data.h string.cpp svm.cpp svm_c_linear.cpp svm_c_linear_dcd.cpp svm_multiclass_linear.cpp svm_struct.cpp svr_linear_trainer.cpp symmetric_matrix_cache.cpp te.cpp tester...
Hence, in order to batch the execution of independent kernels, we can run each of them in a separate stream. In particular, in the above example we could create 1024 CUDA™ streams using the function cudaStreamCreate(), then preface each call to cublas<t>gemm() with a call to cublas...
Note: The non-deterministic behavior of multi-stream execution is due to library optimizations in selecting internal workspace for the routines running in parallel streams. To avoid this effect user can either: ‣ provide a separate workspace for each used stream using the cublasSetWorkspace() ...