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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. The authors propose leveraging Domain-Specific Languages (DSLs) to map high-level application code to heterogeneous devices. To demonstrate the potential of this approach they 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.
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