Petri nets: an introduction
Petri Net Theory and the Modeling of Systems
Petri Net Theory and the Modeling of Systems
Petri Nets and Grafcet: Tools for Modelling Discrete Event Systems
Petri Nets and Grafcet: Tools for Modelling Discrete Event Systems
Petri Net Representations in Metabolic Pathways
Proceedings of the 1st International Conference on Intelligent Systems for Molecular Biology
Brook for GPUs: stream computing on graphics hardware
ACM SIGGRAPH 2004 Papers
Parallel simulation of petri nets on desktop pc hardware
WSC '05 Proceedings of the 37th conference on Winter simulation
Petri Net Based Descriptions for Systematic Understanding of Biological Pathways
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Scan primitives for GPU computing
Proceedings of the 22nd ACM SIGGRAPH/EUROGRAPHICS symposium on Graphics hardware
Scalable Parallel Programming with CUDA
Queue - GPU Computing
Foundations of Systems Biology: Using Cell Illustrator and Pathway Databases
Foundations of Systems Biology: Using Cell Illustrator and Pathway Databases
Long time-scale simulations of in vivo diffusion using GPU hardware
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
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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 7{\times} for a model containing 1,600 cells.