The power of ARM64 in public clouds

Qingye Jiang, Young Choon Lee, Albert Y. Zomaya

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

1 Citation (Scopus)

Abstract

ARM processors, with their low power consumption and heat dissipation, have been highly successful in embedded systems. In the recent past, there have been attempts to adopt these energy-efficient processors for servers in data centers. However, a fundamental question remains open with ARM-based systems on server side is whether they are capable of handling compute-intensive workloads at scale. This paper gives our answer to this question with an empirical approach. We study the performance characteristics of the Amazon Graviton Processor-an ARM64 processor with the Cortex-A72 micro-architecture-using the A1 (Graviton) product family on AWS EC2, with comparisons to the I3 and M5 product families based on Intel Xeon processors. We use a combination of micro benchmark and performance counters to identify the lack of L3 cache and the slower memory access speed limit Graviton's capability in achieving higher performance. We confirm Graviton's capability in handling various large-scale horizontally scalable compute-intensive workloads, including multi-tier web service, video transcoding and terabyte scale sorting. In our large-scale evaluations, the test worker fleet has up to 1600 vCPU cores, which is by far the largest ARM64 cluster that has been reported. We observe that the A1 product family achieves the same price-performance in multi-tier web service, up to 37% cost saving in video transcoding, and up to 65% cost saving in terabyte scale sorting, as compared with the I3 and M5 product families.

Original languageEnglish
Title of host publicationProceedings - 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020
EditorsLaurent Lefevre, Carlos A. Varela, George Pallis, Adel N. Toosi, Omer Rana, Rajkumar Buyya
Place of PublicationLos Alamitos, CA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages459-468
Number of pages10
ISBN (Electronic)9781728160955
DOIs
Publication statusPublished - 2020
Event20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020 - Melbourne, Australia
Duration: 11 May 202014 May 2020

Publication series

NameProceedings - 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020

Conference

Conference20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020
CountryAustralia
CityMelbourne
Period11/05/2014/05/20

Keywords

  • Indexes
  • Benchmark testing
  • Instruction sets
  • Cloud computing
  • Data centers
  • Computer architecture

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