A Representation of Membrane Computing with a Clustering Algorithm on the Graphical Processing Unit

Abstract

Long-timescale simulations of biological processes such as photosynthesis or attempts to solve NP-hard problems such as traveling salesman, knapsack, Hamiltonian path, and satisfiability using membrane systems without appropriate parallelization can take hours or days. Graphics processing units (GPU) deliver an immensely parallel mechanism to compute general-purpose computations. Previous studies mapped one membrane to one thread block on GPU. This is disadvantageous given that when the quantity of objects for each membrane is small, the quantity of active thread will also be small, thereby decreasing performance. While each membrane is designated to one thread block, the communication between thread blocks is needed for executing the communication between membranes. Communication between thread blocks is a time-consuming process. Previous approaches have also not addressed the issue of GPU occupancy. This study presents a classification algorithm to manage dependent objects and membranes based on the communication rate associated with the defined weighted network and assign them to sub-matrices. Thus, dependent objects and membranes are allocated to the same threads and thread blocks, thereby decreasing communication between threads and thread blocks and allowing GPUs to maintain the highest occupancy possible. The experimental results indicate that for 48 objects per membrane, the algorithm facilitates a 93-fold increase in processing speed compared to a 1.6-fold increase with previous algorithms.

Simulation of Spiking Neural P Systems with Sparse Matrix-Vector Operations

“…Usually, parallel, P system simulators make use of ad-hoc representations, tailored for a certain variant [26][27][28]. In order to ease the simulation of static SNP system and its deployment to parallel environments, a matrix representation was introduced [19].…”

“…Simulators for specific solutions have been also defined in the literature [5,25]. Moreover, this is not unique for SNP systems, many simulators for other P system variants have been accelerated on GPUs [26][27][28].…”

P System–Based Clustering Methods Using NoSQL Databases

“…In [28], the authors presented a classification algorithm involving GPUs, allocating dependent objects and membranes to the same threads and thread blocks to decrease the communication between these threads and thread blocks and to allow GPUs to maintain the highest occupancy possible. In [29], the authors presented a review of 65 nature-inspired algorithms used for automatic clustering with their main components in the formulation of the metaheuristics.…”