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A Domain-Specific Approach To Heterogeneous Parallelism

Hassan Chafi, Arvind K. Sujeeth, Kevin J. Brown, HyoukJoong Lee, Anand R. Atreya, Kunle Olukotun
Pervasive Parallelism Laboratory, Stanford University
Proceedings of the 16th ACM symposium on Principles and practice of parallel programming, 2011, p.35-46
@conference{chafi2011domain,

   title={A domain-specific approach to heterogeneous parallelism},

   author={Chafi, H. and Sujeeth, A.K. and Brown, K.J. and Lee, H.J. and Atreya, A.R. and Olukotun, K.},

   booktitle={Proceedings of the 16th ACM symposium on Principles and practice of parallel programming},

   pages={35–46},

   year={2011},

   organization={ACM}

}

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Exploiting heterogeneous parallel hardware currently requires mapping application code to multiple disparate programming models. Unfortunately, general-purpose programming models available today can yield high performance but are too low-level to be accessible to the average programmer. We propose leveraging domainspecific languages (DSLs) to map high-level application code to heterogeneous devices. To demonstrate the potential of this approach we present OptiML, a DSL for machine learning. OptiML programs are implicitly parallel and can achieve high performance on heterogeneous hardware with no modification required to the source code. For such a DSL-based approach to be tractable at large scales, better tools are required for DSL authors to simplify language creation and parallelization. To address this concern, we introduce Delite, a system designed specifically for DSLs that is both a framework for creating an implicitly parallel DSL as well as a dynamic runtime providing automated targeting to heterogeneous parallel hardware. We show that OptiML running on Delite achieves single-threaded, parallel, and GPU performance superior to explicitly parallelized MATLAB code in nearly all cases.
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