Feb 20, 2024 · We propose a row decomposition (RoDe)-based approach to optimize the two kernels on GPUs, using the standard Compressed Sparse Row (CSR) format.
We propose a row decomposition (RoDe)-based approach to optimize the two kernels on GPUs, using the standard Compressed Sparse Row (CSR) format.
Feb 28, 2024 · We show how mesh-connected n×n-processor arrays with dynamically reconfigurable busses can be used efficiently to compute the product of sparse ...
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[PDF] Design Principles for Sparse Matrix Multiplication on the GPU
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We show, both theoretically and experimentally, that the proposed SpMM is a better fit for the GPU than previous approaches. We identify a key memory access ...
We propose a novel approach to iterated sparse matrix dense matrix multiplication, a fundamental computational kernel in scientific computing and graph neural ...
Furthermore, we examine the performance of these different sparse matrix multiply algorithms running across multiple GPUs in a distributed memory environment.
Missing: Decomposition- | Show results with:Decomposition-
The approach to SpGEMM in [3] is based on a decomposition of SpGEMM into 3 phases: expansion, sorting, and contraction (ESC). The ESC formulation of the ...
We propose a novel approach to iterated sparse matrix dense matrix multiplication, a fundamental computational kernel in scientific computing and graph ...
This work presents a GPU SpGEMM algorithm that particularly focuses on load balancing, memory pre-allocation for the result matrix, and parallel insert ...
Using the row-wise product approach and the new sparse format, we describe the design of MatRaptor, a highly efficient accelerator architecture for SpGEMM.