Balancing Performance, Flexibility, and Scalability in a Parallel Computing Platform for Membrane Computing Applications

Abstract

Summary. Membrane computing investigates models of computation inspired by certain features of biological cells. To exploit the performance advantage of the large-scale parallelism of membrane computing models, it is necessary to execute them on a parallel computing platform. However, it is an open question whether it is feasible to develop a parallel computing platform for membrane computing applications that significantly outperforms equivalent sequential computing platforms while still achieving acceptable flexibility and scalability. To move closer to an answer to this question, we have investigated a novel approach to the development of a parallel computing platform for membrane computing applications that has the potential to deliver a good balance between performance, flexibility and scalability. This approach involves the use of reconfigurable hardware and an intelligent software component that is able to configure the hardware to suit the specific properties of the membrane computing model to be executed. We have developed a prototype computing platform called Reconfig-P based on the approach. Reconfig-P is the first computing platform of its type to implement parallelism at both the system and region levels. In this paper, we describe Reconfig-P and evaluate its performance, flexibility and scalability. Theoretical and empirical results suggest that the implementation approach on which Reconfig-P is based is a viable means of attaining a good balance between performance, flexibility and scalability in a parallel computing platform for membrane computing applications.

Research of Metabolic P System Field Programmable Gate Array Implementation

“…Currently, the research of hardware implementation techniques of MP systems is lacking, although there are hardware implementations of similar membrane computing systems (Nguyen et al 2007). The possibilities of MP system implementation in hardware were theoretically examined (Guiraldelli, Manca 2015b), but techniques for multiple system implementation in a single device or the quality characteristics of such implementations are still not investigated enough.…”

Simulating P Systems on GPU Devices: A Survey

“…The first three features are considered as the most important quality attributes for computing platforms for Membrane Computing applications [24]:…”

“…A factor promoting one attribute might decrease another one. In fact, flexibility usually comes at the expense of both performance and scalability [24].…”

“…Concerning the types of computing platforms for simulating P systems, there are mainly three approaches [24]:…”

Parallel Numerical P systems using a MIMD based architecture

“…Van Hguyen [18] describes a very interesting system where the P system is defined in a macro language and then translated into Handel-C before being compiled into a netlist and placed onto an FPGA. Similarly Alonso [19] and Martinez [20] independently created Transition P systems where rule priorities are calculated using predefined logic (created in VHDL on an FPGA).…”