A tuning framework for software-managed memory hierarchies

  • Authors:

  • Manman Ren;Ji Young Park;Mike Houston;Alex Aiken;William J. Dally

  • Affiliations:

  • Stanford University, Stanford, CA, USA;Stanford University, Stanford, CA, USA;Stanford University, Stanford, CA, USA;Stanford University, Stanford, CA, USA;Stanford University, Stanford, CA, USA

  • Venue:
  • Proceedings of the 17th international conference on Parallel architectures and compilation techniques
  • Year:
  • 2008

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Abstract

Achieving good performance on a modern machine with a multi-level memory hierarchy, and in particular on a machine with software-managed memories, requires precise tuning of programs to the machine's particular characteristics. A large program on a multi-level machine can easily expose tens or hundreds of inter-dependent parameters which require tuning, and manually searching the resultant large, non-linear space of program parameters is a tedious process of trial-and-error. In this paper we present a general framework for automatically tuning general applications to machines with software-managed memory hierarchies. We evaluate our framework by measuring the performance of benchmarks that are tuned for a range of machines with different memory hierarchy configurations: a cluster of Intel P4 Xeon processors, a single Cell processor, and a cluster of Sony Playstation3's.