39.4 Conclusion The scan operation is a simple and powerful parallel primitive with a broad range of applications. In this chapter we have explained an efficient implementation of scan using CUDA, which achieves a significant speedup compared to a sequential implementation on a fast CPU, and ...
TransposeTest.cpp: File I/O and parallel transpose tests MultTiming.cpp: Parallel SpGEMM tests IndexingTest.cpp: Various sparse matrix indexing usages ParIOTest.cppParallel reading of arbitrary labeled tuples with SpParMat::ReadGeneralizedTuples() ...
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Nevertheless, we decided to conduct our initial tests on CUDA capable devices. The contributions of this paper are as follows: We present an extended version of the concept of mapping a rectangular grid of elements onto a triangular part of a matrix. This mapping is achieved using various ...
However, you would have to transpose the data matrix to guarantee coalesced memory accesses. Here, we can achieve the same without transposition. For the sake of simplicity, assume that n=32 such that we can process all time ticks within a single warp. Our to be implemented algorithm can ...
'test_matrix_nms_op', 'test_matmul_transpose_reshape_fuse_pass', 'test_matmul_mkldnn_op', 'test_matmul_bf16_mkldnn_op', 'test_match_matrix_tensor_op', 'test_lookup_table_dequant_op', 'test_logging_utils', 'test_logger', 'test_lod_tensor_array_ops', 'test_l...
We present that swCUDA is adaptive to the flexible CUDA kernel programming. 4.1 General kernel translation The general matrix multiplication (GEMM) computes a scalar-matrix-matrix product and adds the result to a scalar-matrix product with CUDA code in Polybench suite is shown in figure 6, ...
A“GPU-kernel”, as the term is used herein, comprises code and/or processing logic that executes one or more functions or operations on a GPU. For example, GPU-kernels are developed for matrix multiplication, matrix transpose, subdivision, equation solving, and numerous other operations. In th...
3. GPU Parallel Implementation Based on CUDA The proposed parallel implementation starts with the characteristics of GPU programming. Matrix operations are always performed by the GPU, and the memory transfer between the CPU (host) and the GPU (device) is reduced as much as possible. A few crit...
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