B
Brian Vinter
Researcher at University of Copenhagen
Publications - 94
Citations - 1288
Brian Vinter is an academic researcher from University of Copenhagen. The author has contributed to research in topics: Python (programming language) & Grid. The author has an hindex of 17, co-authored 93 publications receiving 1151 citations. Previous affiliations of Brian Vinter include University of Copenhagen Faculty of Science & Odense University.
Papers
More filters
Proceedings ArticleDOI
CSR5: An Efficient Storage Format for Cross-Platform Sparse Matrix-Vector Multiplication
Weifeng Liu,Brian Vinter +1 more
TL;DR: CSR5 (Compressed Sparse Row 5), a new storage format, which offers high-throughput SpMV on various platforms including CPUs, GPUs and Xeon Phi, is proposed for real-world applications such as a solver with only tens of iterations because of its low-overhead for format conversion.
Posted Content
CSR5: An Efficient Storage Format for Cross-Platform Sparse Matrix-Vector Multiplication
Weifeng Liu,Brian Vinter +1 more
TL;DR: In this article, the authors proposed CSR5 (Compressed Sparse Row 5), a new storage format, which offers high-throughput SpMV on various platforms including CPUs, GPUs and Xeon Phi.
Proceedings ArticleDOI
An Efficient GPU General Sparse Matrix-Matrix Multiplication for Irregular Data
Weifeng Liu,Brian Vinter +1 more
TL;DR: This work presents a GPU SpGEMM algorithm that particularly focuses on load balancing, memory pre-allocation for the result matrix, and parallel insert operations of the nonzero entries that is experimentally found to be the fastest GPU merge approach.
Journal ArticleDOI
A framework for general sparse matrix-matrix multiplication on GPUs and heterogeneous processors
Weifeng Liu,Brian Vinter +1 more
TL;DR: This work proposes a framework for SpGEMM on GPUs and emerging CPU-GPU heterogeneous processors using the CSR format, and proposes an efficient parallel insert method for long rows of the resulting matrix and develops a heuristic-based load balancing strategy.
Book ChapterDOI
A Synchronization-Free Algorithm for Parallel Sparse Triangular Solves
TL;DR: This paper proposes a novel approach for SpTRSV in which the ordering between components is naturally enforced within the solution stage, and is an order of magnitude faster for the preprocessing stage than existing methods.