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.
Highlights
Membrane systems are a class of computational models that inspired their computation from cell biology
Simulation timescales for different numbers of objects with the same iterations vary from milliseconds to hours
Previous studies mapped each membrane and its corresponding objects to one thread block and the associated threads, resulting in decreased efficiency when the number of membrane objects was smaller than the number of threads per thread blocks required to achieve maximum occupancy
Summary
Membrane systems are a class of computational models that inspired their computation from cell biology. The main elements of a membrane system include (i) structure, including its delimiting compartments; (ii) multisets of objects; (iii) biochemically-inspired rules. Membrane systems are based on the parallel and distributed systems found in biological cells, with both the objects and membranes located within the system capable of being processed by the same set of rules [11,12]. Multiple parts, such as a skin and the membranes, are compartmentalized inside an active. Various sets of objects (comparable to biochemical materials) membrane system. Various sets of objects (comparable to biochemical materials) and groups of evolutionary rules (identical to responses) exist inside of membranes’ compartments and groups of evolutionary rules (identical to responses) exist inside of membranes’ compartments (Figure 1).
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