PetaBricks: A Language and Compiler for Algorithmic Choice
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Edelman_Petabricks a.pdf
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Author(s)
Ansel, Jason Andrew
Chan, Cy
Wong, Yee Lok
Olszewski, Marek Krystyn
Zhao, Qin
Edelman, Alan
Amarasinghe, Saman P.
Date Issued
June 2009
Journal
Proceedings of the 2009 ACM SIGPLAN conference on Programming language design and implementation
Publisher
Association for Computing Machinery
Citation
Jason Ansel, Cy Chan, Yee Lok Wong, Marek Olszewski, Qin Zhao, Alan Edelman, and Saman Amarasinghe. 2009. PetaBricks: a language and compiler for algorithmic choice. In Proceedings of the 2009 ACM SIGPLAN conference on Programming language design and implementation (PLDI '09). ACM, New York, NY, USA, 38-49.
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Author's final manuscript
Abstract
It is often impossible to obtain a one-size-fits-all solution for high performance algorithms when considering different choices for data distributions, parallelism, transformations, and blocking. The best solution to these choices is often tightly coupled to different architectures, problem sizes, data, and available system resources. In some cases, completely different algorithms may provide the best performance. Current compiler and programming language techniques are able to change some of these parameters, but today there is no simple way for the programmer to express or the compiler to choose different algorithms to handle different parts of the data. Existing solutions normally can handle only coarse-grained, library level selections or hand coded cutoffs between base cases and recursive cases.
We present PetaBricks, a new implicitly parallel language and compiler where having multiple implementations of multiple algorithms to solve a problem is the natural way of programming. We make algorithmic choice a first class construct of the language. Choices are provided in a way that also allows our compiler to tune at a finer granularity. The PetaBricks compiler autotunes programs by making both fine-grained as well as algorithmic choices. Choices also include different automatic parallelization techniques, data distributions, algorithmic parameters, transformations, and blocking.
Additionally, we introduce novel techniques to autotune algorithms for different convergence criteria. When choosing between various direct and iterative methods, the PetaBricks compiler is able to tune a program in such a way that delivers near-optimal efficiency for any desired level of accuracy. The compiler has the flexibility of utilizing different convergence criteria for the various components within a single algorithm, providing the user with accuracy choice alongside algorithmic choice.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mathematics
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DOI of Published Version
https://doi.org/10.1145/1543135.1542481
