Automatic performance tuning of sparse matrix kernels

By R. Vuduc, J. Demmel. 2003

298 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/92229143e62f1f8eecdd29920836851111b6bcce

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Citations in the corpus

EIE: Efficient Inference Engine on Compressed Deep Neural Network (2016, methodology): “To exploit the sparsity of activations we store our encoded sparse weight matrix W in a variation of compressed sparse column (CSC) format [24].”
CSR5: An Efficient Storage Format for Cross-Platform Sparse Matrix-Vector Multiplication (2015, unknown): “0.1h [31, 32] kernels.”
Model-driven autotuning of sparse matrix-vector multiply on GPUs (2010, methodology): “…Autotuning of Sparse Matrix-Vector Multiply on GPUs Jee W. Choi Amik Singh Richard W. Vuduc Georgia Institute of Technology, School Indian Institute of Technology Roorkee, Georgia Institute of Technology, of Electrical and Computer Engineering Department of Electronics and Computer…”
SCNN: An accelerator for compressed-sparse convolutional neural networks (2017, methodology): “To compress the weights and activations, we use variants of previously proposed compressed sparse matrix representations [15, 33].”
The potential of the cell processor for scientific computing (2005, methodology): “2 Evaluation Matrices In order to effectively evaluate SpMV performance, we examine six synthetic matrices, as well as ten real matrices used in numerical calculations from the Bebop SPARSITY suite [3,5] (four unsymmetric and six symmetric).”
OSKI: A library of automatically tuned sparse matrix kernels (2005, methodology): “We use this indirection because the best data structure and code transformations on modern hardware may be diﬃcult to determine, even in seemingly simple cases [4, 1].”
HiCOO: Hierarchical Storage of Sparse Tensors (2018, background): “There are many options for sparse matrices that tradeoff size, speed, and “fit” to the nonzero structure of a given input matrix and requirements of the application [1–17].”
Optimization of sparse matrix-vector multiplication on emerging multicore platforms (2007, background): “These patterns include blocks [10], variable or mixtures of differently-sized blocks [6] diagonals, which may be especially well-suited to machines with SIMD and vector units [19], dense subtriangles arising in sparse triangular solve [22], and symmetry [11], and combinations.”