Which sparse matrix representations are most efficient for linear algebra computations?
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| Papers (9) | Insight |
|---|---|
05 Mar 2011 61 Citations | We propose a system-independent representation of sparse matrix formats that allows a compiler to generate efficient, system-specific code for sparse matrix operations. |
01 Feb 2020 99 Citations | To address this problem, this paper proposes an efficient sparse matrix multiplication accelerator architecture, SpArch, which jointly optimizes the data locality for both input and output matrices. |
| For some sparse matrix representations this procedure is laborious. We present an efficient method for assembling large irregular sparse matrices where the nonzero elements have to be assembled by adding together contributions and updating the individual elements in random order. | |
| We describe a unique combination of algorithms and data representation that provides high performance and strict error control in blocked sparse matrix algebra. | |
30 Mar 1998 52 Citations | We show that a rather simple sequential cache-efficient algorithm provides significantly better performance than existing algorithms for sparse matrix multiplication. |
| In all cases tested, the new methods are significantly faster than the established sparse matrix techniques. | |
| Our algorithms incur less communication overhead and are more scalable than any previously known parallel formulation of sparse matrix factorization. | |
20 Citations | In this paper we propose a scheme, called SPAR, for efficiently storing and performing computations on sparse matrices. |
| We propose a novel fully algebraic sparse matrix solve algorithm. |
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