Automatically Scheduling Halide Image Processing Pipelines

Ravi Teja Mullapudi, CMU
Andrew Adams, Google
Dillon Sharlet, Google
Jonathan Ragan-Kelley, Stanford
Kayvon Fatahalian, CMU

ACM Transactions on Graphics, 35(4), July 2016
Proceedings of ACM SIGGRAPH 2016

Abstract

The Halide image processing language has proven to be an effective system for authoring high-performance image processing code. Halide programmers need only provide a high-level strategy for mapping an image processing pipeline to a parallel machine (a schedule), and the Halide compiler carries out the mechanical task of generating platform-specific code that implements the schedule. Unfortunately, designing high-performance schedules for complex image processing pipelines requires substantial knowledge of modern hardware architecture and code-optimization techniques. In this paper we provide an algorithm for automatically generating high-performance schedules for Halide programs. Our solution extends the function bounds analysis already present in the Halide compiler to automatically perform locality and parallelism-enhancing global program transformations typical of those employed by expert Halide developers. The algorithm does not require costly (and often impractical) auto-tuning, and, in seconds, generates schedules for a broad set of image processing benchmarks that are performance-competitive with, and often better than, schedules manually authored by expert Halide developers on server and mobile CPUs, as well as GPUs.

Paper

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Code

Halide Autoscheduler branch on Github