Optimization principles and application performance evaluation of a multithreaded GPU using CUDA
University of Illinois Urbana-Champaign · Nvidia (United States) · +1 more institution
Abstract
GPUs have recently attracted the attention of many application developers as commodity data-parallel coprocessors. The newest generations of GPU architecture provide easier programmability and increased generality while maintaining the tremendous memory bandwidth and computational power of traditional GPUs. This opportunity should redirect efforts in GPGPU research from ad hoc porting of applications to establishing principles and strategies that allow efficient mapping of computation to graphics hardware. In this work we discuss the GeForce 8800 GTX processor's organization, features, and generalized optimization strategies. Key to performance on this platform is using massive multithreading to utilize the…
Citation impact
- FWCI
- 117.74
- Percentile
- 100%
- References
- 27
Authors
6- SRShane RyooCorresponding
University of Illinois Urbana-Champaign
- CRChristopher Rodrigues
University of Illinois Urbana-Champaign
- SSSara S. Baghsorkhi
University of Illinois Urbana-Champaign
- SSSam S. Stone
University of Illinois Urbana-Champaign
- DBDavid B. Kirk
Nvidia (United States), Nvidia (United Kingdom)
Topics & keywords
- Computer science
- Speedup
- Parallel computing
- Multithreading
- Coprocessor
- Porting
- Thread (computing)
- Memory bandwidth