Data-Parallel Computing

Authors:
Chas. Boyd
Affiliations:
Microsoft
Venue:
Queue - GPU Computing
Year:
2008

Citing 3
Cited 2

Linear algebra operators for GPU implementation of numerical algorithms

ACM SIGGRAPH 2003 Papers
Glift: Generic, efficient, random-access GPU data structures

ACM Transactions on Graphics (TOG)
GPUTeraSort: high performance graphics co-processor sorting for large database management

Proceedings of the 2006 ACM SIGMOD international conference on Management of data

Fast Parallel Markov Clustering in Bioinformatics Using Massively Parallel Computing on GPU with CUDA and ELLPACK-R Sparse Format

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Heterogeneous Computing in Economics: A Simplified Approach

Computational Economics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Users always care about performance. Although often it's just a matter of making sure the software is doing only what it should, there are many cases where it is vital to get down to the metal and leverage the fundamental characteristics of the processor.