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. 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.

This paper investigates shallow embedding of DSLs by means of online partial evaluation. To this end, we present a novel online partial evaluator for continuation-passing style languages. We argue that it has, in contrast to prior work, a predictable termination policy that works well in practice. We present our approach formally using a continuation-passing variant of PCF and prove its termination properties. We evaluate our technique experimentally in the field of visual and high-performance computing and show that our evaluator produces highly specialized and efficient code for CPUs as well as GPUs that matches the performance of hand-tuned expert code.