R
Richard Vuduc
Researcher at Georgia Institute of Technology
Publications - 150
Citations - 7317
Richard Vuduc is an academic researcher from Georgia Institute of Technology. The author has contributed to research in topics: Sparse matrix & Speedup. The author has an hindex of 38, co-authored 142 publications receiving 6520 citations. Previous affiliations of Richard Vuduc include Lawrence Livermore National Laboratory & University of California, Berkeley.
Papers
More filters
Journal ArticleDOI
OSKI: A Library of Automatically Tuned Sparse Matrix Kernels
TL;DR: An overview of OSKI is provided, which is based on research on automatically tuned sparse kernels for modern cache-based superscalar machines, and the primary aim of this interface is to hide the complex decision-making process needed to tune the performance of a kernel implementation for a particular user's sparse matrix and machine.
Journal ArticleDOI
Optimization of sparse matrix-vector multiplication on emerging multicore platforms
TL;DR: This work examines sparse matrix-vector multiply (SpMV) - one of the most heavily used kernels in scientific computing - across a broad spectrum of multicore designs, and presents several optimization strategies especially effective for the multicore environment.
Proceedings ArticleDOI
Model-driven autotuning of sparse matrix-vector multiply on GPUs
TL;DR: A performance model-driven framework for automated performance tuning (autotuning) of sparse matrix-vector multiply (SpMV) on systems accelerated by graphics processing units (GPU) and shows that the model can identify the implementations that achieve within 15% of those found through exhaustive search.
Proceedings ArticleDOI
Optimization of sparse matrix-vector multiplication on emerging multicore platforms
TL;DR: In this article, the authors examine sparse matrix-vector multiply (SpMV) kernels across a broad spectrum of multicore designs and present several optimization strategies especially effective for the multicore environment, and demonstrate significant performance improvements compared to existing state-of-the-art serial and parallel SpMV implementations.
Journal ArticleDOI
Sparsity: Optimization Framework for Sparse Matrix Kernels
TL;DR: This paper discusses the optimization of two operations: a sparse matrix times a dense vector and a sparse matrices times a set of dense vectors, and describes the different optimizations and parameter selection techniques and evaluates them on several machines using over 40 matrices.