Efficient computation on tree data structures is important in compilers, numeric computations, and web browser layout engines. Efficiency is achieved by statically scheduling the computation into a small number of tree traversals and by performing the traversals in parallel when possible. Manual design of such traversals leads to bugs, as observed in web browsers. Automatic schedulers avoid these bugs but they currently cannot explore a space of legal traversals, which prevents exploring the trade-offs between parallelism and minimizing the number of traversals. We describe Hecate, a synthesizer of tree traversals that can produce both serial and parallel traversals. A key feature is that the synthesizer is extensible by the programmer who can define a template for new kinds of traversals. Hecate is constructed as a solver-aided domain-specific language, meaning that the synthesizer is generated automatically by translating the tree traversal DSL to an SMT solver that synthesizes the traversals. We improve on the general-purpose solver-aided architecture with a scheduling-specific symbolic evaluation that maintains the engineering advantages solver-aided design but generates efficient ILP encoding that is much more efficient to solve than SMT constraints. On the set of Grafter problems, Hecate synthesizes traversals that trade off traversal fusion to exploit parallelism. Additionally, Hecate allows defining a tree data structure with an arbitrary number of children. Together, parallelism and data structure improvements accelerate the computation 2× on a tree rendering problem. Finally, Hecate’s domain-specific symbolic compilation accelerates synthesis 3× compared to the general-purpose compilation to an SMT solver; when scheduling a CSS engine traversal, this ILP-based synthesis executes orders of magnitude faster.

@inproceedings{ChenLFB22,
  title = {Tree traversal synthesis using domain-specific symbolic compilation},
  author = {Yanju Chen and Junrui Liu and Yu Feng and Rastislav Bodik},
  booktitle = {ASPLOS},
  year = {2022},
  doi = {10.1145/3503222.3507751}
}