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    <abstract language="eng">This paper advocates programming high-performance code using partial evaluation. We present a clean-slate programming system with a simple, annotation-based, online partial evaluator that operates on a CPS-style intermediate representation. Our system exposes code generation for accelerators (vectorization/parallelization for CPUs and GPUs) via compiler-known higher-order functions that can be subjected to partial evaluation. This way, generic implementations can be instantiated with target-specific code at compile time.&#13;
&#13;
In our experimental evaluation we present three extensive case studies from image processing, ray tracing, and genome sequence alignment. We demonstrate that using partial evaluation, we obtain high-performance implementations for CPUs and GPUs from one language and one code base in a generic way. The performance of our codes is mostly within 10%, often closer to the performance of multi man-year, industry-grade, manually-optimized expert codes that are considered to be among the top contenders in their fields.</abstract>
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      <first_name>Klaas</first_name>
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      <first_name>André</first_name>
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      <first_name>Bertil</first_name>
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      <first_name>Richard</first_name>
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      <first_name>James</first_name>
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      <first_name>Philipp</first_name>
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      <first_name>Simon</first_name>
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    <subject>
      <language>eng</language>
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    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
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    <author>
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      <first_name>Sebastian</first_name>
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      <first_name>Jan</first_name>
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    <collection role="persons" number="43674">Membarth, Richard</collection>
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</export-example>
