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Runtime Engine for Dynamic Profile Guided Stride Prefetching

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Abstract

Stride prefetching is recognized as an important technique to improve memory access performance. The prior work usually profiles and/or analyzes the program behavior offline, and uses the identified stride patterns to guide the compilation process by injecting the prefetch instructions at appropriate places. There are some researches trying to enable stride prefetching in runtime systems with online profiling, but they either cannot discover cross-procedural prefetch opportunity, or require special supports in hardware or garbage collection. In this paper, we present a prefetch engine for JVM (Java Virtual Machine). It firstly identifies the candidate load operations during just-in-time (JIT) compilation, and then instruments the compiled code to profile the addresses of those loads. The runtime profile is collected in a trace buffer, which triggers a prefetch controller upon a protection fault. The prefetch controller analyzes the trace to discover any stride patterns, then modifies the compiled code to inject the prefetch instructions in place of the instrumentations. One of the major advantages of this engine is that, it can detect striding loads in any virtual code places for both regular and irregular code, not being limited with plain loop or procedure scopes. Actually we found the cross-procedural patterns take about 30% of all the prefetchings in the representative Java benchmarks. Another major advantage of the engine is that it has runtime overhead much smaller (the maximal is less than 4.0%) than the benefits it brings. Our evaluation with Apache Harmony JVM shows that the engine can achieve an average 6.2% speed-up with SPECJVM98 and DaCapo on Intel Pentium 4 platform, in spite of the runtime overhead.

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Authors and Affiliations

  1. Department of Computer Science, University of Science and Technology of China, Hefei, 230027, China

    Qiong Zou

  2. Key Laboratory of Computer System and Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Qiong Zou & Long-Bing Zhang

  3. Intel China Research Center, Beijing, 100190, China

    Xiao-Feng Li

Authors
  1. Qiong Zou
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  2. Xiao-Feng Li
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  3. Long-Bing Zhang
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Corresponding author

Correspondence to Qiong Zou.

Additional information

Supported by the National Natural Science Foundation of China under Grant Nos. 60673146, 60603049, 60736012, and 60703017, the National High Technology Development 863 Program of China under Grant No. 2006AA010201 and No. 2007AA01Z114, the National Basic Research Program of China under Grant No. 2005CB321601.

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Zou, Q., Li, XF. & Zhang, LB. Runtime Engine for Dynamic Profile Guided Stride Prefetching. J. Comput. Sci. Technol. 23, 633–643 (2008). https://doi.org/10.1007/s11390-008-9159-2

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  • DOI: https://doi.org/10.1007/s11390-008-9159-2

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