Pruning hardware evaluation space via correlation-driven application similarity analysis

  • Authors:

  • Rosario Cammarota;Arun Kejariwal;Paolo D'Alberto;Sapan Panigrahi;Alexander V. Veidenbaum;Alexandru Nicolau

  • Affiliations:

  • University of California at Irvine, Irvine, CA;Yahoo! Inc., Sunnyvale, CA;Yahoo! Inc., Sunnyvale, CA;Yahoo! Inc., Sunnyvale, CA;University of California at Irvine, Irvine, CA;University of California at Irvine, Irvine, CA

  • Venue:
  • Proceedings of the 8th ACM International Conference on Computing Frontiers
  • Year:
  • 2011

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Abstract

System evaluation is routinely performed in industry to select one amongst a set of different systems to improve performance of proprietary applications. However, a wide range of system configurations is available every year on the market. This makes an exhaustive system evaluation progressively challenging and expensive. In this paper we propose a novel similarity-based methodology for system selection. Our methodology prunes the set of candidate systems by eliminating those systems that are likely to reduce performance of a given proprietary application. The pruning process relies on applications that are similar to a given application of interest whose performance on the candidte systems is known. This obviates the need to install and run the given application on each and every candidate system. The concept of similarity we introduce is performance centric. For a given application, we compute the Pearson's correlation between different types of resource stall and cycles per instruction. We refer to the vector of Pearson's correlation coefficients as an application signature. Next, we assess similarity between two applications as Spearman's correlation between their respective signature. We use the former type of correlation to quantify the association between pipeline stalls and cycles per instruction, whereas we use the latter type of correlation to quantify the association of two signatures, hence to assess similarity, based on the difference in terms of rank ordering of their components. We evaluate the proposed methodology on three different micro-architectures, viz., Intel's Harpertown, Nehalem and Westmere, using industry-standard SPEC CINT2006. We assess performance centric similarity among applications in SPEC CINT2006. We show how our methodology clusters applications with common performance issues. Finally, we show how to use the notion of similarity among applications to compare the three architectures with respect to a given Yahoo! property.