Performance Analysis of MPI (mpi4py) on Diskless Cluster Environment in Ubuntu

Chowdhury, Sayma Sultana; Marium-e-jannat,; Shoeb, Abu Awal Md

doi:10.5120/9764-3701

Cited by 2 publications

(1 citation statement)

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“…On the other hand, the constant increase in processors clock speeds have attained a saturation point due to physical device limitations. As a result, efforts to introduce more computational power in computing has shifted towards parallel computer architecture [2]. A parallel computer architecture composed of p processing nodes is theoretically capable of providing a p times speedup than the single processor architecture with a carefully optimized version of a serial program.…”

Section: Introduction He Explosion Of Data In the Recent Times Alongmentioning

confidence: 99%

Performance Evaluation of ARM-based versus x86- based Processors in High Performance Computing Clusters

Kumar,

Rajput,

Kumar

et al. 2023

JISR-C

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With an increasing need for enormous amount of computing power, different approaches for building inexpensive supercomputers have emerged. Decreasing costs and continuous technology scaling has led to the emergence of computers with ARM processors which are considered as a possible alternative to traditional x86 processors for creating High Performance Computing (HPC) systems. The aim of this research is to carry out a detailed performance evaluation of a cluster of ARM-based computers against a comparable low-cost cluster of x86-based computers and investigates whether these ARM processors are capable to substitute x86 processors for creating HPC clusters. Our methodology is based on experimental evaluation by setting up two cluster architectures. To compare the performances of both clusters, two complex applications were implemented: approximation of mathematical constant PI (3.14), a matrix multiplication program. Our analysis focused on execution time and speedup as performance metrics. From the results we observed that maximum performance achieved by both clusters is when the number of MPI processes are equal to the number of processing nodes on the cluster thereby allowing a one-to-one mapping of the tasks with minimal overhead. If we compare the maximum performances of both clusters in terms of execution time, the x86-Cluster performed 1.16× and 2.42× faster than the ARM-Cluster for pi_calculation and matrix_mul benchmarks. However, we noted that the ARM-based cluster exhibited superior speedup compared to the x86-based cluster, potentially attributed to the greater number of cores per node used in the ARM cluster. From the comparative performance evaluation, we conclude that ARM processors substantially lag behind x86 processors not only because of their limited resources but due to major difference between their instruction sets and architecture especially when it comes to memory-bound operations. This research underscores the challenges and opportunities associated with ARM processors in the context of HPC clusters, offering valuable insights to the scientific community looking for cost-effective computing solutions.

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Section: Introduction He Explosion Of Data In the Recent Times Alongmentioning

confidence: 99%