Sparsity-Specific Code Optimization using Expression Trees

Philipp Herholz, Xuan Tang, Teseo Schneider, Shoaib Kamil, Daniele Panozzo, Olga Sorkine-Hornung

Research output: Contribution to journalArticlepeer-review


We introduce a code generator that converts unoptimized C++ code operating on sparse data into vectorized and parallel CPU or GPU kernels. Our approach unrolls the computation into a massive expression graph, performs redundant expression elimination, grouping, and then generates an architecture-specific kernel to solve the same problem, assuming that the sparsity pattern is fixed, which is a common scenario in many applications in computer graphics and scientific computing. We show that our approach scales to large problems and can achieve speedups of two orders of magnitude on CPUs and three orders of magnitude on GPUs, compared to a set of manually optimized CPU baselines. To demonstrate the practical applicability of our approach, we employ it to optimize popular algorithms with applications to physical simulation and interactive mesh deformation.

Original languageEnglish (US)
Article number175
JournalACM Transactions on Graphics
Issue number5
StatePublished - May 13 2022


  • Code optimisation
  • sparse computation

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design


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