A novel graph transformation strategy for optimizing SpTRSV on CPUs
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CitationYılmaz, B. A novel graph transformation strategy for optimizing SpTRSV on CPUs. Concurrency and Computation: Practice and Experience, e7761.
Sparse triangular solve (SpTRSV) is an extensively studied computational kernel. An important obstacle in parallel SpTRSV implementations is that in some parts of a sparse matrix the computation is serial. By transforming the dependency graph, it is possible to increase the parallelism of the parts that lack it. In this work, we present a novel graph transformation strategy to increase the parallelism degree of a sparse matrix and compare it to our previous strategy. It is seen that our transformation strategy can provide a speedup as high as 1.42x$$ 1.42x $$.