JOURNAL ARTICLE

Strategies and software support for the management of hardware performance counters.

  • Published In: Software: Practice & Experience, 2023, v. 53, n. 10. P. 1928 1 of 3

  • Database: Applied Science & Technology Source Ultimate 2 of 3

  • Authored By: Carnà, Stefano; Marotta, Romolo; Pellegrini, Alessandro; Quaglia, Francesco 3 of 3

Abstract

Hardware performance counters (HPCs) are facilities offered by most off‐the‐shelf CPU architectures. They are a vital support to post‐mortem performance profiling and are exploited by standard tools such as Linux or Intel V‐Tune. Nevertheless, an increasing number of application domains (e.g., simulation, task‐based high‐performance computing, or cybersecurity) are exploiting them to perform different activities, such as self‐tuning, autonomic optimization, and/or system inspection. This repurposing of HPCs can be difficult, for example, because of the overhead for extracting relevant information. This overhead might render any online or self‐tuning activity ineffective. This article discusses various practical strategies to exploit HPCs beyond post‐mortem profiling, suitable for different application contexts. The presented strategies are accompanied by a general primer on HPCs usage on Linux. We also provide reference x86 (both Intel and AMD) implementations targeting the Linux kernel, upon which we present an experimental assessment of the viability of our proposals. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Software: Practice & Experience. 2023/10, Vol. 53, Issue 10, p1928
  • Document Type:Article
  • Subject Area:Computer Science
  • Publication Date:2023
  • ISSN:00380644
  • DOI:10.1002/spe.3236
  • Accession Number:171875094
  • Copyright Statement:Copyright of Software: Practice & Experience is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.