Abstract
Communicating data within the graphic processing unit (GPU) memory system and between the CPU and GPU are major bottlenecks in accelerating Krylov solvers on GPUs. Communication-avoiding techniques reduce the communication cost of Krylov subspace methods by computing several vectors of a Krylov subspace 'at once,' using a kernel called 'matrix powers.' The matrix powers kernel is implemented on a recent generation of NVIDIA GPUs and speedups of up to 5.7 times are reported for the communication-avoiding matrix powers kernel compared to the standards prase matrix vector multiplication (SpMV) implementation.
| Original language | English (US) |
|---|---|
| Article number | 6514719 |
| Pages (from-to) | 1749-1752 |
| Number of pages | 4 |
| Journal | IEEE Transactions on Magnetics |
| Volume | 49 |
| Issue number | 5 |
| DOIs | |
| State | Published - 2013 |
| Externally published | Yes |
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Electrical and Electronic Engineering
Keywords
- Graphic processors
- Krylov solvers
- numerical algorithms
- parallel algorithms