WISE
Şerif Yeşil, Azin Heidarshenas, Adam Morrison, Josep Torrellas
Abstract
Sparse Matrix-Vector Multiplication (SpMV) is an essential sparse kernel. Numerous methods have been developed to accelerate SpMV. However, no single method consistently gives the highest performance across a wide range of matrices. For this reason, a performance prediction model is needed to predict the best SpMV method for a given sparse matrix. Unfortunately, predicting SpMV's performance is challenging due to the diversity of factors that impact it.
Topics & Concepts
Computer scienceSparse matrixKernel (algebra)Multiplication (music)Matrix (chemical analysis)Parallel computingMathematicsMaterials scienceCombinatoricsQuantum mechanicsComposite materialGaussianPhysicsGraph Theory and AlgorithmsParallel Computing and Optimization TechniquesDistributed and Parallel Computing Systems