Name
Title | ||
|---|---|---|
High performance hybrid functional Petri net simulations of biological pathway models on CUDA. |
Abstract | ||
|---|---|---|
Hybrid functional Petri nets are a wide-spread tool for representing and simulating biological models. Due to their potential of providing virtual drug testing environments, biological simulations have a growing impact on pharmaceutical research. Continuous research advancements in biology and medicine lead to exponentially increasing simulation times, thus raising the demand for performance accelerations by efficient and inexpensive parallel computation solutions. Recent developments in the field of general-purpose computation on graphics processing units (GPGPU) enabled the scientific community to port a variety of compute intensive algorithms onto the graphics processing unit (GPU). This work presents the first scheme for mapping biological hybrid functional Petri net models, which can handle both discrete and continuous entities, onto compute unified device architecture (CUDA) enabled GPUs. GPU accelerated simulations are observed to run up to 18 times faster than sequential implementations. Simulating the cell boundary formation by Delta-Notch signaling on a CUDA enabled GPU results in a speedup of approximately 7x for a model containing 1,600 cells. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TCBB.2010.118 | IEEE/ACM Trans. Comput. Biology Bioinform. |
Keywords | Field | DocType |
biological pathway models,pharmaceutical research,gpu result,continuous entity,biological simulation,inexpensive parallel computation solution,general-purpose computation,continuous research advancement,cell boundary formation,biological model,high performance hybrid functional,hybrid functional petri net,computational modeling,bioinformatics,modeling,parallel computer,petri nets,parallel algorithms,instruction sets,computer graphics,computer model,gpgpu,graphical user interfaces | Petri net,Instruction set,CUDA,Computer science,Computational science,Artificial intelligence,Computer graphics,Speedup,Parallel algorithm,Parallel computing,General-purpose computing on graphics processing units,Bioinformatics,Graphics processing unit,Machine learning | Journal |
Volume | Issue | ISSN |
8 | 6 | 1557-9964 |
Citations | PageRank | References |
5 | 0.65 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Georgios Chalkidis | 1 | 5 | 0.65 |
| Masao Nagasaki | 2 | 368 | 26.22 |
| Satoru Miyano | 3 | 2406 | 250.71 |