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Towards Faster Reasoners by Using Transparent Huge Pages

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Principles and Practice of Constraint Programming (CP 2020)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12333))

Abstract

Various state-of-the-art automated reasoning (AR) tools are widely used as backend tools in research of knowledge representation and reasoning as well as in industrial applications. In testing and verification, those tools often run continuously or nightly. In this work, we present an approach to reduce the runtime of AR tools by 10% on average and up to 20% for long running tasks. Our improvement addresses the high memory usage that comes with the data structures used in AR tools, which are based on conflict driven no-good learning. We establish a general way to enable faster memory access by using the memory cache line of modern hardware more effectively. Therefore, we extend the standard C library (glibc) by dynamically allowing to use a memory management feature called huge pages. Huge pages allow to reduce the overhead that is required to translate memory addresses between the virtual memory of the operating system and the physical memory of the hardware. In that way, we can reduce runtime, which in turn decreases costs of running AR tools and applications with similar memory access patterns by linking the tool against this new glibc library when compiling it. In every day industrial applications, runtime savings allow to include more detailed verification tasks, getting better results of any-time optimization algorithms with a bound execution time, and save energy during nightly software builds. To back up the claimed speed-up, we present experimental results for tools that are commonly used in the AR community, including the domains ASP, hardware and software BMC, MaxSAT, and SAT.

The work has been supported by the Austrian Science Fund (FWF), Grants P32441 and W1255. We would like to thank the anonymous reviewers for very detailed feedback and their suggestions. Special thanks go to the reviewer who provided comments on CP solvers and propagators. The implementation is available at github:daajoe/thp_docker_build.

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Notes

  1. 1.

    We use a sampling approach of CPU performance counters for TLB misses with the system tool perf.

  2. 2.

    Our latest implementation is publicly available at github:daajoe/thp_docker_build and the glib patch at github:conp-solutions/thp. An upstream to the glibc library is in progress and we are in contact with the glibc maintainers.

  3. 3.

    MUS benchmarks are available at cril.univ-artois.fr/SAT11.

  4. 4.

    Note that we initially also ran experiments with an earlier kernel 3.10.0-693. There, non-THP runtime results were comparable, but improvements were slightly smaller.

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Authors and Affiliations

  1. School of Engineering Sciences, TU Dresden, Dresden, Germany

    Johannes K. Fichte

  2. Dresden, Germany

    Norbert Manthey & Julian Stecklina

  3. Algorithms and Complexity Group, TU Wien, Vienna, Austria

    André Schidler

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  1. Johannes K. Fichte
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  2. Norbert Manthey
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Correspondence to Johannes K. Fichte .

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    Helmut Simonis

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Fichte, J.K., Manthey, N., Stecklina, J., Schidler, A. (2020). Towards Faster Reasoners by Using Transparent Huge Pages. In: Simonis, H. (eds) Principles and Practice of Constraint Programming. CP 2020. Lecture Notes in Computer Science(), vol 12333. Springer, Cham. https://doi.org/10.1007/978-3-030-58475-7_18

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