Name
Abstract | ||
|---|---|---|
The cardiac bidomain model is a popular approach to study electrical behavior of tissues and simulate interactions between the cells by solving partial differential equations. The iterative and data parallel model is an ideal match for the parallel architecture of Graphic Processing Units (GPUs). In this study, we evaluate the effectiveness of architecture-specific optimizations and fine grained parallelization strategies, completely port the model to GPU, and evaluate the performance of single-GPU and multi-GPU implementations. Simulating one action potential duration (350 msec real time) for a 256脳256脳256 tissue takes 453 hours on a high-end general purpose processor, while it takes 664 seconds on a four-GPU based system including the communication and data transfer overhead. This drastic improvement (a factor of 2460脳) will allow clinicians to extend the time-scale of simulations from milliseconds to seconds and minutes; and evaluate hypotheses in a shorter amount of time that was not feasible previously. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/s11227-010-0540-x | The Journal of Supercomputing |
Keywords | Field | DocType |
Bidomain model,Cardiac tissue,Graphics processing unit,Multi-GPU | Bidomain model,GPU cluster,General purpose,Data transmission,Computer science,Parallel computing,Millisecond,Graphics processing unit,Partial differential equation,Parallel architecture | Journal |
Volume | Issue | ISSN |
59 | 3 | 0920-8542 |
Citations | PageRank | References |
12 | 0.79 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Venkata Krishna Nimmagadda | 1 | 12 | 0.79 |
| Ali Akoglu | 2 | 157 | 29.40 |
| Salim Hariri | 3 | 2593 | 184.23 |
| Talal Moukabary | 4 | 15 | 2.23 |