Abstract
To date, parallel simulation algorithms for spiking neural P (SNP) systems are based on a matrix representation. This way, the simulation is implemented with linear algebra operations, which can be easily parallelized on high performance computing platforms such as GPUs. Although it has been convenient for the first generation of GPU-based simulators, such as CuSNP, there are some bottlenecks to sort out. For example, the proposed matrix representations of SNP systems lead to very sparse matrices, where the majority of values are zero. It is known that sparse matrices can compromise the performance of algorithms since they involve a waste of memory and time. This problem has been extensively studied in the literature of parallel computing. In this paper, we analyze some of these ideas and apply them to represent some variants of SNP systems. We also provide a new simulation algorithm based on a novel compressed representation for sparse matrices. We also conclude which SNP system variant better suits our new compressed matrix representation.
Highlights
Membrane computing [1,2] is an interdisciplinary research area in the intersection of computer science and cellular biology mainly [3], and with many other fields such as engineering, neuroscience, systems biology, chemistry, etc
Spiking neural P (SNP) systems [4] are a type of P system composed of a directed graph inspired by how neurons are interconnected by axons and synapses in the brain
We introduce compressed representations for the simulation of SNP systems based on sparse vector-matrix operations
Summary
Membrane computing [1,2] is an interdisciplinary research area in the intersection of computer science and cellular biology mainly [3], and with many other fields such as engineering, neuroscience, systems biology, chemistry, etc. Most simulation algorithms are based on matrices and vector representations, and consists of a set of linear algebra operations. This way, parallel simulators can be efficiently implemented, since matrix-vector multiplications are easy to parallelize. Without loss of generality, that these matrix representations of SNP systems fit well to the highly parallel architecture of these devices This have been harnessed already by introducing CuSNP, a set of simulators for SNP systems implemented with CUDA [21,22,23,24]. We introduce compressed representations for the simulation of SNP systems based on sparse vector-matrix operations. The paper is structured as follows: Section 2 provides required concepts for the methods and algorithms here defined; Section 3 defines the designs of the representations; Section 4 contains the detailed algorithms based on the compressed representations; Section 5 shows the results on complexity analyses of the algorithms; Section 6 provides final conclusions, remarks, and plans of future work
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