Abstract
We highlight the trends leading to the increased appeal of using hybrid multicore + GPU systems for high performance computing. We present a set of techniques that can be used to develop efficient dense linear algebra algorithms for these systems. We illustrate the main ideas with the development of a hybrid LU factorization algorithm where we split the computation over a multicore and a graphics processor, and use particular techniques to reduce the amount of pivoting and communication between the hybrid components. This results in an efficient algorithm with balanced use of a multicore processor and a graphics processor.
| Original language | English |
|---|---|
| Pages (from-to) | 232-240 |
| Number of pages | 9 |
| Journal | Parallel Computing |
| Volume | 36 |
| Issue number | 5-6 |
| DOIs | |
| State | Published - May 2010 |
Funding
Part of this work was supported by the US National Science Foundation and the US Department of Energy. We thank NVIDIA and NVIDIA’s Professor Partnership Program for their hardware donations. We thank also Jim Demmel and Vasily Volkov from UC Berkeley, and Massimiliano Fatica from NVIDIA for helpful discussions related to GPU computing.
| Funders | Funder number |
|---|---|
| US Department of Energy | |
| US National Science Foundation |
Keywords
- Dense linear algebra
- Graphics processing units
- Hybrid computing
- Multicore processors
- Parallel algorithms