Dynamic parallelization of single-threaded binary programs using speculative slicing

  • Authors:

  • Cheng Wang;Youfeng Wu;Edson Borin;Shiliang Hu;Wei Liu;Dave Sager;Tin-fook Ngai;Jesse Fang

  • Affiliations:

  • Intel Corporation, Santa Clara, CA, USA;Intel Corporation, Santa Clara, CA, USA;Intel Corporation, Santa Clara, CA, USA;Intel Corporation, Santa Clara, CA, USA;Intel Corporation, Santa Clara, CA, USA;Intel Corporation, Hillsboro, OR, USA;Intel Corporation, Santa Clara, CA, USA;Intel Corporation, Santa Clara, CA, USA

  • Venue:
  • Proceedings of the 23rd international conference on Supercomputing
  • Year:
  • 2009

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Abstract

The performance of single-threaded programs and legacy binary code is of critical importance in many everyday applications. However, neither can hardware multi-core processors directly speed up single-threaded programs, nor can software automatic parallelizing compilers effectively parallelize legacy binary code and irregular applications. In this paper, we propose a framework and a set of algorithms to dynamically parallelize single-threaded binary programs. Our parallelization is based on program slicing and explores both instruction-level parallelism (ILP) and thread-level parallelism (TLP). To significantly reduce the critical path of the parallel slices, our slicing algorithms exploit speculation to cut rare dependences, and use well-designed program transformations to expose parallelism. Furthermore, because we transparently parallelize binary code at runtime, we perform slicing only on program hot regions. Our experiments demonstrate that the proposed speculative slicing approach extracts more parallelism than any known slicing based parallelization schemes. For the SPEC2000 benchmarks, we can achieve 3x parallelism with infinite number of threads, and 1.8x parallelism with 4 threads.