Deterministic parallel random-number generation for dynamic-multithreading platforms
Name
Leiserson_Deterministic parallel.pdf
Size
248.42 KB
Format
Adobe PDF
Checksum (MD5)
25287d3eaeecc0654ab0b412e4fb397a
Author(s)
Leiserson, Charles E.
Sukha, Jim
Schardl, Tao Benjamin
Date Issued
February 2012
Journal
Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming (PPoPP '12)
Publisher
Association for Computing Machinery (ACM)
Citation
Charles E. Leiserson, Tao B. Schardl, and Jim Sukha. 2012. Deterministic parallel random-number generation for dynamic-multithreading platforms. In Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming (PPoPP '12). ACM, New York, NY, USA, 193-204.
Version
Author's final manuscript
Abstract
Existing concurrency platforms for dynamic multithreading do not provide repeatable parallel random-number generators. This paper proposes that a mechanism called pedigrees be built into the runtime system to enable efficient deterministic parallel random-number generation. Experiments with the open-source MIT Cilk runtime system show that the overhead for maintaining pedigrees is negligible. Specifically, on a suite of 10 benchmarks, the relative overhead of Cilk with pedigrees to the original Cilk has a geometric mean of less than 1%.
We persuaded Intel to modify its commercial C/C++ compiler, which provides the Cilk Plus concurrency platform, to include pedigrees, and we built a library implementation of a deterministic parallel random-number generator called DotMix that compresses the pedigree and then "RC6-mixes" the result. The statistical quality of DotMix is comparable to that of the popular Mersenne twister, but somewhat slower than a nondeterministic parallel version of this efficient and high-quality serial random-number generator. The cost of calling DotMix depends on the "spawn depth" of the invocation. For a naive Fibonacci calculation with n=40 that calls DotMix in every node of the computation, this "price of determinism" is a factor of 2.65 in running time, but for more realistic applications with less intense use of random numbers -- such as a maximal-independent-set algorithm, a practical samplesort program, and a Monte Carlo discrete-hedging application from QuantLib -- the observed "price" was less than 5%. Moreover, even if overheads were several times greater, applications using DotMix should be amply fast for debugging purposes, which is a major reason for desiring repeatability.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/2145816.2145841
