Mango-IO: I/O Metrics Consistency Analysis

Research output: Contribution to book/conference proceedings/anthology/reportConference contributionContributedpeer-review

Contributors

Abstract

Performance tools are inseparable from complex HPC applications’ performance analysis and engineering life cycles. Due to the application’s complexity, various performance analysis tools are created to serve different analysis purposes and provide a deeper look at certain aspects of the applications. Although these tools might operate differently, having coherent information and consistent metrics across all tools is mandatory for ensuring analysis continuity. It is common for performance analysts to switch their usual performance tools due to various reasons and limitations. In this work, we look specifically at the I/O performance analysis tools landscape and introduce Mango-IO to verify the result consistencies between tools and provide tool-agnostic metrics calculation methods. Our analysis and case study provides lesson learned and guideline for ensuring measurement continuity and comparability.

Details

Original languageGerman
Title of host publication2023 IEEE International Conference on Cluster Computing Workshops (CLUSTER Workshops)
PublisherIEEE
Pages18-24
Number of pages7
ISBN (print)979-8-3503-7063-8
Publication statusPublished - 31 Oct 2023
Peer-reviewedYes

Workshop

Title2023 IEEE International Conference on Cluster Computing Workshops
Abbreviated titleCLUSTER Workshops 2023
Duration31 October 2023
Website
Degree of recognitionInternational event
LocationHilton Santa Fe Historic Plaza
CitySanta Fe
CountryUnited States of America

External IDs

Scopus 85179622959

Keywords

Keywords

  • Measurement, Conferences, Switches, Cluster computing, Performance analysis, Complexity theory, Guidelines