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.
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Notes
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See [21, Appendix A] for a detailed description of the TRSs.
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Acknowledgment
This work is carried out in the context of the NWO AVVA project 612.001751. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GeForce Titan RTX used for this research.
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van Eerd, J., Groote, J.F., Hijma, P., Martens, J., Wijs, A. (2021). Term Rewriting on GPUs. In: Hojjat, H., Massink, M. (eds) Fundamentals of Software Engineering. FSEN 2021. Lecture Notes in Computer Science(), vol 12818. Springer, Cham. https://doi.org/10.1007/978-3-030-89247-0_12
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