Ali Maroosi

MEMBRANE COMPUTING INSPIRED GENETIC ALGORITHM ON MULTI-CORE PROCESSORS

Membrane computing is a branch of natural computing. Several studies have recently attempted to utilize the structure of membrane computing to improve intelligent algorithms. These studies have applied communication rules in membrane models to facilitate information exchange between membranes, thereby improving the performance of those algorithms. However, parallel membrane computing has not yet been considered. This study proposes a membrane computing-inspired genetic algorithm. Similar to previous studies, the algorithm also uses communication rules to facilitate information exchange. In this study, an appropriate membrane computing-inspired genetic algorithm is defined, in which each membrane can be executed over different cores in a parallel manner. The proposed algorithm can be executed over different cores and uses multi-core processing to implement parallel membrane computation. Simulation with a Colville minimization problem shows that the membrane computing inspired genetic algorithm has improved performance, with a mean error of the solution 61.9 times better than genetic algorithm.

Accelerated execution of P systems with active membranes to solve the N-queens problem

The N-queens problem has attracted increasing attention because of its potential applications in different areas, such as parallel memory storage approaches, image processing, and physical and chemical studies. Local search is a powerful method for solving real problems, such as the N-queens problem. Recently, models of P systems with active membranes have been used for local search to solve the N-queens problem. However, there have been insufficient studies of the parallelism of the P-system models with active membranes. In addition, the active membrane systems defined for N queens have several individual membranes that contain one object and no internal rules in each membrane, as well as several communication rules among membranes, which reduce the execution speed. In this study, a new P system model with active membranes is defined for solving the N-queens problem, and multi-core simulation of the proposed membrane system allows the execution of alternative computations in parallel, thus reducing the average time for finding a successful computation. The speed of the proposed model was compared with previous models that used P systems with active membranes for local search. The model contains two membranes, but the inclusion of several objects and rules within each membrane increases the parallelism and performance. This model reduces the number of communication rules required among membranes, and increases the execution speed. This model also increases the parallelism of previous P systems with active membranes when several rules evolve concurrently and more than one queen is exchanged during each step to reach a solution. Multi-core processing has been used to decrease the probability of restarting the P systems and to decrease processing time by distributing the processing of the active membrane on the multi-core. The speed of the proposed model when solving N = 200 queens was almost 1000 times faster than previous methods.

Parallel and distributed computing models on a graphics processing unit to accelerate simulation of membrane systems

Abstract Membrane systems are parallel distributed computing models that are used in a wide variety of areas. Use of a sequential machine to simulate membrane systems loses the advantage of parallelism in Membrane Computing. In this paper, an innovative classification algorithm based on a weighted network is introduced. Two new algorithms have been proposed for simulating membrane systems models on a Graphics Processing Unit (GPU). Communication and synchronization between threads and thread blocks in a GPU are time-consuming processes. In previous studies, dependent objects were assigned to different threads. This increases the need for communication between threads, and as a result, performance decreases. In previous studies, dependent membranes have also been assigned to different thread blocks, requiring inter-block communications and decreasing performance. The speedup of the proposed algorithm on a GPU that classifies dependent objects using a sequential approach, for example with 512 objects per membrane, was 82×, while for the previous approach ( Algorithm 1 ), it was 8.2×. For a membrane system with high dependency among membranes, the speedup of the second proposed algorithm ( Algorithm 3 ) was 12×, while for the previous approach ( Algorithm 1 ) and the first proposed algorithm ( Algorithm 2 ) that assign each membrane to one thread block, it was 1.8×.

Accelerated Simulation of Membrane Computing to Solve the N-queens Problem on Multi-core

Membrane computing or P Systems are distributed and parallel computing device that inspired their computation from cell biology. In this study, a new model of membrane computing with active membranes is defined for solving the N-queens problem. The model contains two membranes, but the inclusion of several objects and rules within each membrane. This model increases the parallelism of previous Membrane computing with active membranes because several rules can evolve concurrently and more than one queen can be exchanged during each step. Number of communication rules are also decreased. Communication rules decrease speed on multi-core processing because communications and synchronizations between threads and cores that are necessary for communication rules are very time consuming process. Multi-core processing is used to exploit the parallelism of membrane computing for solving N-queens problem.

Enhancement of membrane computing model implementation on GPU by introducing matrix representation for balancing occupancy and reducing inter-block communications

Abstract In previous studies, objects of each membrane were assigned to threads of one thread block of the graphic processing unit (GPU). The number of active threads was low if the number of objects inside a membrane was low. This study represents objects of membranes as entities of a matrix. Then a sub-matrix represents the appropriate number of objects assigned to threads of each thread block to balance the load and keep the occupancy high even when the number of objects per membrane is low. The size of the sub-matrix or the appropriate number of active threads is determined automatically. Furthermore, by this approach it is possible to assign more than one membrane to each thread block and to execute communication between membranes in the same thread block without the need for time-consuming inter-block communication. For example, using the previous algorithm, for two objects per membrane the speed up is 0.6×, while for the proposed algorithm the speed up is 32.4×.

Membrane Computing to Enhance Time Efficiency of Minimum Dominating Set

Graph theory is widely used in numerous fields, such as, engineering, physics, social and biological sciences; linguistics etc. The minimum dominating set (MDS) problem is one of the main problems of algorithmic graph theory and has numerous applications especially in graph mining. Since it is NP-hard to solve the MDS problem approximately, much work has been dedicated to central and distributed approximation algorithms for restricted graph classes. In recent research exponential time

Accelerated P systems with active membranes on a graphics processing unit

O(k^{n})

Enhancing the Simulation of Membrane System on the GPU for the N-Queens Problem

O(kn) algorithms are used for some graph classes for solving the MDS problem. In the approach of using the algorithmic tile self-assembly model, the MDS problem has been solved in