Multi-Objective Binary PSO with Kernel P System on GPU

Elkhani, Naeimeh; Muniyandi, Ravie Chandren; Zhang, Gexiang

doi:10.15837/ijccc.2018.3.3282

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…The authors of the large majority of the considered papers used this approach as displayed in Tables 3, 4 and 6. This strategy is easy to implement and it is capable of reducing considerably the execution time of the algorithm [25,[28][29][30][31][32][33][34][35][36][37][38][39][40].…”

Section: Particle-level Parallelizationmentioning

confidence: 99%

Parallelization of Swarm Intelligence Algorithms: Literature Review

Menezes

Kuchen²,

Neto³

2022

Int J Parallel Prog

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Swarm Intelligence (SI) algorithms are frequently applied to tackle complex optimization problems. SI is especially used when good solutions are requested for NP hard problems within a reasonable response time. And when such problems possess a very high dimensionality, a dynamic nature, or present intrinsic complex intertwined independent variables, computational costs for SI algorithms may still be too high. Therefore, new approaches and hardware support are needed to speed up processing. Nowadays, with the popularization of GPU and multi-core processing, parallel versions of SI algorithms can provide the required performance on those though problems. This paper aims to describe the state of the art of such approaches, to summarize the key points addressed, and also to identify the research gaps that could be addressed better. The scope of this review considers recent papers mainly focusing on parallel implementations of the most frequently used SI algorithms. The use of nested parallelism is of particular interest, since one level of parallelism is often not sufficient to exploit the computational power of contemporary parallel hardware. The sources were main scientific databases and filtered accordingly to the set requirements of this literature review.

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Section: Particle-level Parallelizationmentioning

confidence: 99%

Parallelization of Swarm Intelligence Algorithms: Literature Review

Menezes

Kuchen²,

Neto³

2022

Int J Parallel Prog

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“…Many other developments involving the usage of GPUs to speedup the simulation of P systems can be found in the literature: extended simulation of P systems with active membranes [19], kernel P systems [8,14], membrane algorithms [34], evolution-communication P systems with energy [15], fuzzy reasoning spiking neural P systems [18], etc.…”

Section: Parallel Implementationsmentioning

confidence: 99%

Simulation challenges in membrane computing

Valencia-Cabrera¹,

Pérez–Hurtado²,

Amor³

2020

J Membr Comput

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P system simulators are critical tools to enable them as formal modeling framework for real-life applications. Such simulators abstract the concept of P systems in various ways, depending on the needs of the users and the requirements of the specific application. We identify three main levels of abstraction: graphical user interfaces, simulation engines and parallel implementations. In this paper, we survey the state of the art at these levels and discuss the main challenges under consideration for future developments.

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“…Implementing P system parallelism in modern parallel processors is not straightforward, mainly due to its non-deterministic and synchronous nature, but it has been shown that Graphics Processing Units (GPUs) can be employed successfully for this task. For example, some variants of P systems that have been simulated on GPUs are: P systems with active membranes [20], a specific family of P systems with active membranes solving SAT [21,22], kernel P systems on PSO [23], and spiking neural P systems through a matrix representation [24]. In this sense, a project called PMCGPU [25] includes some P system simulators implemented on GPUs.…”

Section: Introductionmentioning

confidence: 99%

Adaptative parallel simulators for bioinspired computing models

Amor

Pérez–Hurtado

Orellana-Martín

et al. 2020

Future Generation Computer Systems

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In the Membrane Computing area, P systems are unconventional devices of computation inspired by the structure and processes taking place in living cells. Main successful P system applications lie in computability and computational complexity theories, as well as in biological modelling. Given that models become too complex to deal with, simulators for P systems are essential tools and their efficiency is critical. In order to handle the diverse situations that may arise during the computation, these simulators have to take into account that worst-case scenarios can happen, even though they rarely occur. As a result, there is a significant loss of performance. In this paper, the concept of adaptative simulation for P systems is introduced to palliate this problem. This is achieved by passing high-level information provided directly by P system model designers to the simulator, helping it to better adapt to the target model. For this purpose, an existing simulator for an ecosystem modelling framework, named Population Dynamics P systems, is extended to include the information of modules, that are usually employed to define ecosystem models. Moreover, the standard description language for P systems, P-Lingua, has been re-engineered in its version 5. It now includes a new syntactical item, called feature, to express this kind of high-level semantic information. Experiments show that this simple adaptative simulator supporting modules as features doubles the performance when running on GPUs and on multicore processors.

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Multi-Objective Binary PSO with Kernel P System on GPU

Cited by 8 publications

References 10 publications

Parallelization of Swarm Intelligence Algorithms: Literature Review

Parallelization of Swarm Intelligence Algorithms: Literature Review

Simulation challenges in membrane computing

Adaptative parallel simulators for bioinspired computing models

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