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View all- Stitt GCampbell D(2020)PANDORAACM Transactions on Embedded Computing Systems10.1145/339189919:5(1-17)Online publication date: 11-Nov-2020
PANDORA: a parallelizing approximation-discovery framework (WIP paper)
Pages 198 - 202
Abstract
In this paper, we introduce PANDORA---a framework that complements existing parallelizing compilers by automatically discovering application- and architecture-specialized approximations. We demonstrate that PANDORA creates approximations that extract massive amounts of parallelism from inherently sequential code by eliminating loop-carried dependencies---a long-time goal of the compiler research community. Compared to exact parallel baselines, preliminary results show speedups ranging from 2.3x to 81x with acceptable error for many usage scenarios.
References
[1]
J. Ansel, Y. L. Wong, C. Chan, M. Olszewski, A. Edelman, and S. Amarasinghe. 2011. Language and compiler support for auto-tuning variableaccuracy algorithms. In International Symposium on Code Generation and Optimization (CGO 2011). 85-96.
[2]
Woongki Baek and Trishul M. Chilimbi. 2010. Green: A Framework for Supporting Energy-conscious Programming Using Controlled Approximation. In Proceedings of the 31st ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI'10). ACM, NewYork, NY, USA, 198-209.
[3]
Michael Carbin, Deokhwan Kim, Sasa Misailovic, and Martin C. Rinard. 2013. Verified Integrity Properties for Safe Approximate Program Transformations. In Proceedings of the ACM SIGPLAN 2013 Workshop on Partial Evaluation and Program Manipulation (PEPM'13). ACM, New York, NY, USA, 63-66.
[4]
Michael Carbin, Sasa Misailovic, and Martin C. Rinard. 2013. Verifying Quantitative Reliability for Programs That Execute on Unreliable Hardware. In Proceedings of the 2013 ACM SIGPLAN International Conference on Object Oriented Programming Systems Languages & Applications (OOPSLA'13). ACM, New York, NY, USA, 33-52.
[5]
Hadi Esmaeilzadeh, Adrian Sampson, Luis Ceze, and Doug Burger. 2012. Neural Acceleration for General-Purpose Approximate Programs. In Proceedings of the 2012 45th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO-45). IEEE Computer Society, Washington, DC, USA, 449-460.
[6]
Félix-Antoine Fortin, François-Michel De Rainville, Marc-André Gardner, Marc Parizeau, and Christian Gagné. 2012. DEAP: Evolutionary Algorithms Made Easy. Journal of Machine Learning Research 13 (July 2012), 2171-2175.
[7]
Milind Girkar and Constantine D. Polychronopoulos. 1995. Extracting Task-level Parallelism. ACM Trans. Program. Lang. Syst. 17, 4 (July 1995), 600-634.
[8]
J. Han and M. Orshansky. 2013. Approximate computing: An emerging paradigm for energy-efficient design. In 2013 18th IEEE European Test Symposium (ETS). 1-6.
[9]
Gregory S. Hornby. 2006. ALPS: The Age-layered Population Structure for Reducing the Problem of Premature Convergence. In Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation (GECCO'06). ACM, New York, NY, USA, 815-822.
[10]
John R. Koza. 1994. Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge, MA, USA.
[11]
Logan Kugler. 2015. Is "Good Enough" Computing Good Enough? Commun. ACM 58, 5 (April 2015), 12-14.
[12]
James McDermott, David R. White, Sean Luke, Luca Manzoni, Mauro Castelli, Leonardo Vanneschi, Wojciech Jaskowski, Krzysztof Krawiec, Robin Harper, Kenneth De Jong, and Una-May O'Reilly. 2012. Genetic Programming Needs Better Benchmarks. In Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation (GECCO '12). ACM, New York, NY, USA, 791-798.
[13]
Sasa Misailovic, Michael Carbin, Sara Achour, Zichao Qi, and Martin C. Rinard. 2014. Chisel: Reliability- and Accuracy-aware Optimization of Approximate Computational Kernels. In Proceedings of the 2014 ACM International Conference on Object Oriented Programming Systems Languages & Applications (OOPSLA'14). ACM, New York, NY, USA, 309-328.
[14]
Sasa Misailovic, Stelios Sidiroglou, and Martin C. Rinard. 2012. Dancing with Uncertainty. In Proceedings of the 2012 ACMWorkshop on Relaxing Synchronization for Multicore and Manycore Scalability (RACES'12). ACM, New York, NY, USA, 51-60.
[15]
T. Moreau, M. Wyse, J. Nelson, A. Sampson, H. Esmaeilzadeh, L. Ceze, and M. Oskin. 2015. SNNAP: Approximate computing on programmable SoCs via neural acceleration. In 2015 IEEE 21st International Symposium on High Performance Computer Architecture (HPCA). 603- 614.
[16]
Lakshminarayanan Renganarayana, Vijayalakshmi Srinivasan, Ravi Nair, and Daniel Prener. 2012. Programming with Relaxed Synchronization. In Proceedings of the 2012 ACM Workshop on Relaxing Synchronization for Multicore and Manycore Scalability (RACES'12). ACM, New York, NY, USA, 41-50.
[17]
Stelios Sidiroglou-Douskos, Sasa Misailovic, Henry Hoffmann, and Martin Rinard. 2011. Managing Performance vs. Accuracy Tradeoffs with Loop Perforation. In Proceedings of the 19th ACM SIGSOFT Symposium and the 13th European Conference on Foundations of Software Engineering (ESEC/FSE'11). ACM, New York, NY, USA, 124-134.
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Published: 23 June 2019
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LCTES '19: 20th ACM SIGPLAN/SIGBED Conference on Languages, Compilers, and Tools for Embedded Systems
June 23, 2019
AZ, Phoenix, USA
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View all- Stitt GCampbell D(2020)PANDORAACM Transactions on Embedded Computing Systems10.1145/339189919:5(1-17)Online publication date: 11-Nov-2020
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