Name
Abstract | ||
|---|---|---|
Modern graphics processing units (GPUs) offer much more computational power than recent CPUs by providing a vast number of simple, data-parallel, multithreaded cores. In this study, we focus on the use of a GPU to perform parallel discrete-event simulation. Our approach is to use a modified service time distribution function to allow more independent events to be processed in parallel. The implementation issues and alternative strategies will be discussed in detail. We describe and compare our experience and results in using Thrust and CUB, two open-source parallel algorithms libraries which resemble the C\({++}\) Standard Template Library, to build our tool. The experimental results show that our implementation can be two orders of magnitude faster than the sequential simulation for large-scale simulation models. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11227-018-2254-4 | The Journal of Supercomputing |
Keywords | Field | DocType |
Parallel simulation, Discrete-event simulation, GPU, CUDA, Thrust/CUB | Graphics,Parallel algorithm,Computer science,CUDA,Parallel computing,Distribution function,Thrust,Independence (probability theory),Standard Template Library,Discrete event simulation | Journal |
Volume | Issue | ISSN |
75.0 | SP8.0 | 1573-0484 |
Citations | PageRank | References |
1 | 0.35 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Janche Sang | 1 | 66 | 13.03 |
| Che-Rung Lee | 2 | 9 | 6.64 |
| Vernon Rego | 3 | 326 | 42.85 |
| Chung-Ta King | 4 | 450 | 74.71 |