SparseLU, A Novel Algorithm and Math Library for Sparse LU Factorization
DescriptionDecomposing sparse matrices into lower and upper triangular matrices (sparse LU factorization) is a key operation in many computational scientific applications. We developed SparseLU, a sparse linear algebra library that implements a new algorithm for LU factorization on general sparse matrices. The new algorithm divides the input matrix into tiles to which OpenMP tasks are created for factorization computation, where only tiles that contain nonzero elements are computed. For comparative performance analysis, we used the reference library SuperLU. Testing was performed on synthetically generated matrices which replicate the conditions of the real-world matrices. SparseLU is able to reach a mean speedup of ∼ 29× compared to SuperLU.
Event Type
Workshop
TimeFriday, 18 November 202211:10am - 11:30am CST
LocationC144-145
W
Accelerator-based Architectures
Algorithms
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Big Data
Data Analytics
Parallel Programming Languages and Models
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