Runtime automatic speculative parallelization

  • Authors:

  • Ben Hertzberg;Kunle Olukotun

  • Affiliations:

  • Dept. of Electrical Engineering, Stanford University;Dept. of Electrical Engineering, Stanford University

  • Venue:
  • CGO '11 Proceedings of the 9th Annual IEEE/ACM International Symposium on Code Generation and Optimization
  • Year:
  • 2011

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Abstract

We present Runtime Automatic Speculative Parallelization (RASP), a technique for the dynamic extraction of speculative threads from a running application in a user-transparent fashion. By leveraging the idle cores in a CMP to analyze, optimize, and participate in the execution of a running sequential program, RASP enables a collection of simpler cores to achieve sequential performance on par with a significantly more complex core. In contrast to other systems for automatic speculative parallelization, RASP uses dynamic binary translation to optimize applications on-the-fly, without any need for recompilation or source code. RASP achieves these speedups without relying on special-purpose hardware support; RASP's dynamic profiling uses a clever variation on conventional performance monitoring, while RASP's speculative execution relies on the same simple hardware support for speculation that has been proposed for simplifying parallel programming. On a simulated cluster of four in-order cores, RASP accelerates SPEC2006 integer benchmarks by an average of 49%, with promising results for scientific and multimedia workloads as well.