Simulation of Resource Usage in Parallel Evolutionary Peptide Optimization using JavaSpaces Technology
Andias Wira-Alam

TL;DR
This paper presents a simulation model using JavaSpaces to analyze resource usage and optimize computation time in parallel evolutionary peptide optimization on Linux clusters.
Contribution
It introduces a simulation framework for resource management in peptide optimization, comparing two evolutionary algorithms with different parallelism parameters.
Findings
Generation-based EA performs better with higher parallelism.
Steady-state EA is more resource-efficient at lower parallelism.
Simulation helps identify optimal resource allocation strategies.
Abstract
Peptide Optimization is a highly complex problem and it takes very long time of computation. This optimization process uses many software applications in a cluster running GNU/Linux Operating System that perform special tasks. The application to organize the whole optimization process had been already developed, namely SEPP (System for Evolutionary Pareto Optimization of Peptides/Polymers). A single peptide optimization takes a lot of computation time to produce a certain number of individuals. However, it can be accelerated by increasing the degree of parallelism as well as the number of nodes (processors) in the cluster. In this master thesis, I build a model simulating the interplay of the programs so that the usage of each resource (processor) can be determined and also the approximated time needed for the overall optimization process. There are two Evolutionary Algorithms that…
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Taxonomy
TopicsAdvanced Multi-Objective Optimization Algorithms · Metaheuristic Optimization Algorithms Research · Evolutionary Algorithms and Applications
