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High Performance Linpack (HPL) Benchmark on Raspberry Pi 4B (8GB) Beowulf Cluster

by Dimitrios Papakyriakou, Ioannis S. Barbounakis
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 25
Year of Publication: 2023
Authors: Dimitrios Papakyriakou, Ioannis S. Barbounakis
10.5120/ijca2023923005

Dimitrios Papakyriakou, Ioannis S. Barbounakis . High Performance Linpack (HPL) Benchmark on Raspberry Pi 4B (8GB) Beowulf Cluster. International Journal of Computer Applications. 185, 25 ( Jul 2023), 11-19. DOI=10.5120/ijca2023923005

@article{ 10.5120/ijca2023923005,
author = { Dimitrios Papakyriakou, Ioannis S. Barbounakis },
title = { High Performance Linpack (HPL) Benchmark on Raspberry Pi 4B (8GB) Beowulf Cluster },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2023 },
volume = { 185 },
number = { 25 },
month = { Jul },
year = { 2023 },
issn = { 0975-8887 },
pages = { 11-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number25/32847-2023923005/ },
doi = { 10.5120/ijca2023923005 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:27:02.154061+05:30
%A Dimitrios Papakyriakou
%A Ioannis S. Barbounakis
%T High Performance Linpack (HPL) Benchmark on Raspberry Pi 4B (8GB) Beowulf Cluster
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 25
%P 11-19
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper focuses on a High Performance Linpack (HPL) benchmarking performance analysis of a state of the Art Beowulf cluster deployed with 24 Raspberry Pi’s 4 (model B) (8GB RAM) computers with a CPU clocked at 1.5 GHz, 64-bit quad-core ARMv8 Cortex-A72. In particular, it presents the increased HPL performance of a Beowulf cluster with the use of the default microSD usage in all the RPi’s in the cluster (SDCS2 64GB micro SDXC 100R A1 C10) compared to using a cluster set-up where the master-node uses a Samsung (1TB) 980 PCI-E 3 NVMe M.2 SSD and the slave-nodes uses each a (256GB) Patriot P300P256GM28 NVME M.2 2280). Moreover, it presents the test results of a multithread execution of a C++ pi calculation program by using one to four cores in one RPi 4 B (8GB) using the above-mentioned microSD. In addition, it presents the test results of a multithread execution of a C++ with MPI (pi) calculation program by using 24 RPi’s 4B with the above-mentioned microSD. In terms of the HPL benchmarking performance testing of a Beowulf cluster where the NVMe M.2 SSD disks are used, RPi 4-B supports and deployed the option to use the entire SSD (MVMe) as a bootable external disk which the boot and root partition (where the actual HPL runs) is hosted in the external SSD. All of them are connected over two Gigabit switches (TL-SG1024D) in a parallel mode of operation so that to build a supercomputer.

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Index Terms

Computer Science
Information Sciences

Keywords

Raspberry Pi 4 cluster Beowulf Cluster Message Passing Interface (MPI) MPICH BLAS High Performance Linpack (HPL) Benchmarking HPL RPi clusters Distributed Systems.

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