Term Rewriting on GPUs

TL;DR

This paper demonstrates a method for implementing term rewriting on GPUs, leveraging parallelism to outperform CPUs when systems exhibit sufficient internal parallelism, and explores future potential for GPU programming via term rewriting.

Contribution

The paper introduces a GPU-based implementation of term rewriting that exploits parallelism, showing significant performance improvements over CPU implementations.

Findings

GPU rewriting outperforms CPU when systems have high parallelism

Potential for further optimization of GPU term rewriting

Term rewriting can serve as a universal programming language for GPUs

Abstract

We present a way to implement term rewriting on a GPU. We do this by letting the GPU repeatedly perform a massively parallel evaluation of all subterms. We find that if the term rewrite systems exhibit sufficient internal parallelism, GPU rewriting substantially outperforms the CPU. Since we expect that our implementation can be further optimized, and because in any case GPUs will become much more powerful in the future, this suggests that GPUs are an interesting platform for term rewriting. As term rewriting can be viewed as a universal programming language, this also opens a route towards programming GPUs by term rewriting, especially for irregular computations.