Proceedings of the Platform for Advanced Scientific Computing Conference 2017
DOI: 10.1145/3093172.3093228
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Increasing the Efficiency of Sparse Matrix-Matrix Multiplication with a 2.5D Algorithm and One-Sided MPI

Abstract: Matrix-matrix multiplication is a basic operation in linear algebra and an essential building block for a wide range of algorithms in various scienti c elds. eory and implementation for the dense, square matrix case are well-developed. If matrices are sparse, with application-speci c sparsity pa erns, the optimal implementation remains an open question. Here, we explore the performance of communication reducing 2.5D algorithms and one-sided MPI communication in the context of linear scaling electronic structur… Show more

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Cited by 20 publications
(17 citation statements)
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“…Solomon and Demmel [28] give a 2.5-dimensional matrix multiplication algorithm, in the sense that the cube is split to (n/c) 1/2 × (n/c) 1/2 × c sub-cubes. A recent work by Lazzaro et al [23] provides a 2.5D algorithm that is also suitable for sparse matrices, which also employs random permutations of rows and columns.…”
Section: Related Workmentioning
confidence: 99%
“…Solomon and Demmel [28] give a 2.5-dimensional matrix multiplication algorithm, in the sense that the cube is split to (n/c) 1/2 × (n/c) 1/2 × c sub-cubes. A recent work by Lazzaro et al [23] provides a 2.5D algorithm that is also suitable for sparse matrices, which also employs random permutations of rows and columns.…”
Section: Related Workmentioning
confidence: 99%
“…e drop o in performance for large node counts is caused by the increased communication/computation ratios, particularly for the sparse linear algebra operations. In this respect, the recent progress in the use of one-sided MPI and a 2.5D algorithm to reduce communication in the DBCSR [45] are promising avenues to improve parallel scalability. Weak scaling performance for these types of electronic structure methods is more di cult to quantify.…”
Section: Resultsmentioning
confidence: 99%
“…The 2 nd order trace resetting density matrix purification method was ported to GPUs [87] by using matrix multiplications provided in the cuBLAS library [88]. Similarly, the GPU-accelerated sparse matrix linear algebra routines in the DBCSR library [89,90] can be employed to compute the density matrix on GPUs.…”
Section: Towards Gpu-accelerated High-performance Computingmentioning
confidence: 99%