PARALLEL PROCESSING FOR INTEGRATION PROBLEM USING HIGH PERFORMANCE COMPUTING WITH OPENMPI : CASE STUDY ON THE NUMBER OF INTEGRATION INTERVAL

• Main Authors: Adji, Teguh Bharata, Pradana, Muhammad Aditya, Sumaryono, Sujoko
• Format: Conference or Workshop Item PeerReviewed
• Language: English
• Published: 2011
• Subjects: Makalah Seminar
• Online Access: https://repository.ugm.ac.id/32423/1/Adji-ISSIT-Teguh_Bharata_Adji-Parallel_Processing-2011.pdf; https://repository.ugm.ac.id/32423/
• Institution: Universitas Gadjah Mada

Along with the increasing need to solve various problems of numerical computation effectively and efficiently, the need for a computer system with high computing capability has increased. Computer systems with high computing capability (high performance computing) offers the ability to integrate the resources of multiple computers to solve a problem of numerical computing. This computer system called computer cluster. The cluster must have the ability to perform computing process by using parallel computing mechanism called message passing. In this study, the implementation of message passing mechanism on a computer cluster is done by using Open Message passing Interface (OpenMPI) application. This study aims to analyze the performance of computer cluster using MPI mechanism in handling the process in parallel computing based on the execution time, speedup, and the efficiency. Parallel computing processes will be executed with OpenMPI application to solve the problems of numerical integration using trapezoidal method. The research method used in this study is by implementing OpenMPI on a Linux-based cluster system and then analyzing the performance of the system in dealing with parallel computing process to solve the numerical integration problems by increasing the number of intervals used in a numerical integration problem. The results of this study shows that by increasing number of intervals, the sequential execution time is initially faster than parallel execution time, although finally reduced significantly with the increase of number of integration interval. Instead, the parallel execution time continues to increase rapidly exceeding the sequential execution time.