• Journal of the Korea Society of Computer and Information
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• v.25 no.1
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• pp.1-11
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• 2020

In this paper, we present a technique that executes MPI parallel programs, that are developed on processor-centric computer architecture, more efficiently on memory-centric computer architecture without program modification. The technique we present here improves performance by replacing low-speed data communication over the network of MPI library functions with high-speed data communication using the property called fast large shared memory of memory-centric computer architecture. The technique we present in the paper is implemented in two programs. The first program is a modified MPI library called MC-MPI-LIB that runs MPI parallel programs more efficiently on memory-centric computer architecture preserving the semantics of MPI library functions. The second program is a simulation program called MC-MPI-SIM that simulates the performance of memory-centric computer architecture on processor-centric computer architecture. We developed and tested the programs on distributed systems environment deployed on Docker based virtualization. We analyzed the performance of several MPI parallel programs and showed that we achieved better performance on memory-centric computer architecture. Especially we could see very high performance on the MPI parallel programs with high communication overhead.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.257-258
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• 2019

본 논문에서는 "프로세서 중심 컴퓨터 구조"에서 개발된 MPI 병렬 프로그램을 수정하지 않고 "메모리 중심 컴퓨터 구조"에서 더 효율적으로 수행시키는 기술을 제안한다. 본 연구에서 제안하는 기술은 메모리 중심 컴퓨터 구조가 가지는 "빠른 대용량 공유 메모리" 특징을 이용하여 MPI 표준 라이브러리가 수행하는 네트워크 통신을 통한 느린 데이터 전달을 공유 메모리를 통한 빠른 데이터 전달로 대체하여 효율성을 얻는다. 본 연구에서 제안한 기술은 도커 가상화 기술을 사용한 분산 시스템 환경에서 MC-MPI-LIB 라이브러리 및 MC-MPI-SIM 시뮬레이터로 구현되었으며 다수의 MPI 병렬 프로그램으로 시험 수행하여 효율성이 있음을 보였다.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.1-7
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• 2022

Recently, as the use of applications such as big data programs and machine learning programs that are driven while generating large amounts of data in the program itself becomes common, the existing main memory alone lacks memory, making it difficult to execute the program quickly. In particular, the need to derive results more quickly has emerged in a situation where it is necessary to analyze whether the entire sequence is genetically altered due to the outbreak of the coronavirus. As a result of measuring performance by applying large-capacity data to a computing system equipped with a self-developed memory pool MOCA host adapter instead of processing large-capacity data from an existing SSD, performance improved by 16% compared to the existing SSD system. In addition, in various other benchmark tests, IO performance was 92.8%, 80.6%, and 32.8% faster than SSD in computing systems equipped with memory pool MOCA host adapters such as SortSampleBam, ApplyBQSR, and GatherBamFiles by task of workflow. When analyzing large amounts of data, such as electrical dielectric pipeline analysis, it is judged that the measurement delay occurring at runtime can be reduced in the computing system equipped with the memory pool MOCA host adapter developed in this research.