optimizations in CuSNP Simulator for Spiking Neural P Systems on CUDA GPUs

optimizations in CuSNP Simulator for Spiking Neural P Systems on CUDA GPUs

Spiking Neural P systems (in short, SNP systems) are computing models based on living neurons. SNP systems are non-deterministic and parallel, hence making use of a parallel processor such as a graphics processing unit (in short, GPU) is a natural candidate for simulations. Matrix representations an...

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Bibliographic Details
Published in:2019 International Conference on High Performance Computing & Simulation (HPCS) pp. 535 - 542
Main Authors: Aboy, Blaine Corwyn D., Bariring, Edward James A., Carandang, Jym Paul, Cabarle, Francis George C., Cruz, Ren Tristan De La, Adorna, Henry N., Martinez-del-Amor, Miguel Angel
Format: Conference Proceeding
Language:English
Published: IEEE 01-07-2019
Subjects:
Biological system modeling
Computational modeling
Computer science
CUDA
Delays
GPU computing
Graphics processing units
Membrane computing
Neurons
Sparse Matrix-Vector
Spiking neural P systems
Urban areas
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Summary:Spiking Neural P systems (in short, SNP systems) are computing models based on living neurons. SNP systems are non-deterministic and parallel, hence making use of a parallel processor such as a graphics processing unit (in short, GPU) is a natural candidate for simulations. Matrix representations and algorithms were previously developed for simulating SNP systems. In this work, our two results extend previous works in simulating SNP systems in the GPU: (a) the number of neurons the simulator can handle is now arbitrary; (b) SNP systems are now represented in a dense instead of sparse way. The impact in terms of time and space of these extensions to the GPU simulator are analysed. As expected, SNP systems with more neurons need more simulation time, although the simulator performance can scale (i.e. perform better) with larger GPUs. The dense representation helps in the simulation of larger systems.
DOI:10.1109/HPCS48598.2019.9188174