TileSpGEMM
Yuyao Niu, Zhengyang Lu, Haonan Ji, Shuhui Song, Zhou Jin, Weifeng Liu
Abstract
Sparse general matrix-matrix multiplication (SpGEMM) is one of the most fundamental building blocks in sparse linear solvers, graph processing frameworks and machine learning applications. The existing parallel approaches for shared memory SpGEMM mostly use the row-row style with possibly good parallelism. However, because of the irregularity in sparsity structures, the existing row-row methods often suffer from three problems: (1) load imbalance, (2) high global space complexity and unsatisfactory data locality, and (3) sparse accumulator selection.
Topics & Concepts
Computer scienceSparse matrixParallel computingMatrix multiplicationAccumulator (cryptography)Parallelism (grammar)LocalityDense graphGraphTheoretical computer scienceAlgorithmQuantum mechanics1-planar graphQuantumGaussianPhysicsPhilosophyLinguisticsLine graphParallel Computing and Optimization TechniquesGraph Theory and AlgorithmsDistributed and Parallel Computing Systems