Abstract
P systems have been proven to be useful as modeling tools in many fields, such as Systems Biology and Ecological Modeling. For such applications, the acceleration of P system simulation is often desired, given the computational needs derived from these kinds of models. One promising solution is to implement the inherent parallelism of P systems on platforms with parallel architectures. In this respect, GPU computing proved to be an alternative to more classic approaches in Parallel Computing. It provides a low cost, and a manycore platform with a high level of parallelism. The GPU has been already employed to speedup the simulation of P systems. In this paper, we look over the available parallel P systems simulators on the GPU, with special emphasis on those included in the PMCGPU project, and analyze some useful guidelines for future implementations and developments.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 269-284 |
| Number of pages | 16 |
| Journal | Fundamenta Informaticae |
| Volume | 136 |
| Issue number | 3 |
| DOIs | |
| State | Published - 2015 |
Keywords
- CUDA
- GPU computing
- Membrane Computing
- P systems
- Parallel Computing
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