Efficient automatic scheduling of imaging and vision pipelines for the GPU
Name
3485486.pdf
Description
Published version
Size
3.12 MB
Format
Adobe PDF
Checksum (MD5)
563a9bec610a10479ae7fd6cf32c3b13
Author(s)
Anderson, Luke
Adams, Andrew
Ma, Karima
Li, Tzu-Mao
Jin, Tian
Ragan-Kelley, Jonathan
Date Issued
2021
Journal
Proceedings of the ACM on Programming Languages
Publisher
Association for Computing Machinery (ACM)
Citation
Anderson, Luke, Adams, Andrew, Ma, Karima, Li, Tzu-Mao, Jin, Tian et al. 2021. "Efficient automatic scheduling of imaging and vision pipelines for the GPU." Proceedings of the ACM on Programming Languages, 5 (OOPSLA).
Version
Final published version
Abstract
We present a new algorithm to quickly generate high-performance GPU implementations of complex imaging and vision pipelines, directly from high-level Halide algorithm code. It is fully automatic, requiring no schedule templates or hand-optimized kernels. We address the scalability challenge of extending search-based automatic scheduling to map large real-world programs to the deep hierarchies of memory and parallelism on GPU architectures in reasonable compile time. We achieve this using (1) a two-phase search algorithm that first ‘freezes’ decisions for the lowest cost sections of a program, allowing relatively more time to be spent on the important stages, (2) a hierarchical sampling strategy that groups schedules based on their structural similarity, then samples representatives to be evaluated, allowing us to explore a large space with few samples, and (3) memoization of repeated partial schedules, amortizing their cost over all their occurrences. We guide the process with an efficient cost model combining machine learning, program analysis, and GPU architecture knowledge. We evaluate our method’s performance on a diverse suite of real-world imaging and vision pipelines. Our scalability optimizations lead to average compile time speedups of 49x (up to 530x). We find schedules that are on average 1.7x faster than existing automatic solutions (up to 5x), and competitive with what the best human experts were able to achieve in an active effort to beat our automatic results.
MIT Department
Koch Institute for Integrative Cancer Research at MIT
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3485486
