Compiling High Performance Recursive Filters
Name
recfilter.pdf
Description
Accepted version
Size
3.55 MB
Format
Adobe PDF
Checksum (MD5)
8b84e78fac0a686bd694469314be1d65
Author(s)
Chaurasia, Gaurav
Ragan-Kelley, Jonathan
Paris, Sylvain
Drettakis, George
Durand, Fredo
Date Issued
2015
Publisher
Association for Computing Machinery (ACM)
Citation
Chaurasia, Gaurav, Ragan-Kelley, Jonathan, Paris, Sylvain, Drettakis, George and Durand, Fredo. 2015. "Compiling High Performance Recursive Filters."
Version
Author's final manuscript
Abstract
© 2015 ACM. Infinite impulse response (IIR) or recursive filters, are essential for image processing because they turn expensive large-footprint convolutions into operations that have a constant cost per pixel regardless of kernel size. However, their recursive nature constrains the order in which pixels can be computed, severely limiting both parallelism within a filter and memory locality across multiple filters. Prior research has developed algorithms that can compute IIR filters with image tiles. Using a divide-and-recombine strategy inspired by parallel prefix sum, they expose greater parallelism and exploit producer-consumer locality in pipelines of IIR filters over multidimensional images. While the principles are simple, it is hard, given a recursive filter, to derive a corresponding tile-parallel algorithm, and even harder to implement and debug it. We show that parallel and locality-aware implementations of IIR filter pipelines can be obtained through program transformations, which we mechanize through a domain-specific compiler. We show that the composition of a small set of transformations suffices to cover the space of possible strategies. We also demonstrate that the tiled implementations can be automatically scheduled in hardwarespecific manners using a small set of generic heuristics. The programmer specifies the basic recursive filters, and the choice of transformation requires only a few lines of code. Our compiler then generates high-performance implementations that are an order of magnitude faster than standard GPU implementations, and outperform hand tuned tiled implementations of specialized algorithms which require orders of magnitude more programming effort-a few lines of code instead of a few thousand lines per pipeline.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/2790060.2790063
