Accelerating the LOBPCG method on GPUs using a blocked sparse matrix vector product
Pages: 75 - 82
Published: Apr 12, 2015
Abstract
This paper presents a heterogeneous CPU-GPU implementation for a sparse iterative eigensolver -- the Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG). For the key routine generating the Krylov search spaces via the product of a sparse matrix and a block of vectors, we propose a GPU kernel based on a modified sliced ELLPACK format. Blocking a set of vectors and processing them simultaneously accelerates the computation of a set of...
Paper Details
Title
Accelerating the LOBPCG method on GPUs using a blocked sparse matrix vector product
Published Date
Apr 12, 2015
Pages
75 - 82
Citation AnalysisPro
You’ll need to upgrade your plan to Pro
Looking to understand the true influence of a researcher’s work across journals & affiliations?
- Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
- Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.
Notes
History