论文标题
在GPU上重写术语
Term Rewriting on GPUs
论文作者
论文摘要
我们提出了一种在GPU上实施术语重写的方法。我们通过让GPU反复对所有字母进行大规模平行评估来做到这一点。我们发现，如果“重写系统”一词表现出足够的内部并行性，则GPU的重写显着优于CPU。由于我们希望我们的实施能够进一步优化，并且在任何情况下，GPU都会在将来变得更加强大，因此这表明GPU是一个有趣的术语重写平台。由于可以将术语重写视为通用编程语言，因此这也可以通过术语重写打开编程GPU的途径，尤其是对于不规则计算。
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.
