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| Content Provider | ACM Digital Library |
|---|---|
| Author | Lee, HyoukJoong Sujeeth, Arvind K. Chafi, Hassan Brown, Kevin J. Atreya, Anand R. Olukotun, Kunle |
| Abstract | 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 domain-specific 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. |
| Starting Page | 35 |
| Ending Page | 46 |
| Page Count | 12 |
| File Format | |
| ISBN | 9781450301190 |
| DOI | 10.1145/1941553.1941561 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2011-02-12 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Parallel programming Dynamic optimizations Runtimes Domain-specific languages |
| Content Type | Text |
| Resource Type | Article |
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