Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code

Riyadh Baghdadi, Jessica Ray, Malek Ben Romdhane, Emanuele Del Sozzo, Abdurrahman Akkas, Yunming Zhang, Patricia Suriana, Shoaib Kamil, Saman Amarasinghe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper introduces Tiramisu, a polyhedral framework designed to generate high performance code for multiple platforms including multicores, GPUs, and distributed machines. Tiramisu introduces a scheduling language with novel commands to explicitly manage the complexities that arise when targeting these systems. The framework is designed for the areas of image processing, stencils, linear algebra and deep learning. Tiramisu has two main features: it relies on a flexible representation based on the polyhedral model and it has a rich scheduling language allowing fine-grained control of optimizations. Tiramisu uses a four-level intermediate representation that allows full separation between the algorithms, loop transformations, data layouts, and communication. This separation simplifies targeting multiple hardware architectures with the same algorithm. We evaluate Tiramisu by writing a set of image processing, deep learning, and linear algebra benchmarks and compare them with state-of-the-art compilers and hand-tuned libraries. We show that Tiramisu matches or outperforms existing compilers and libraries on different hardware architectures, including multicore CPUs, GPUs, and distributed machines.

Original languageEnglish (US)
Title of host publicationCGO 2019 - Proceedings of the 2019 IEEE/ACM International Symposium on Code Generation and Optimization
EditorsTipp Moseley, Alexandra Jimborean, Mahmut Taylan Kandemir
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages193-205
Number of pages13
ISBN (Electronic)9781728114361
DOIs
StatePublished - Mar 5 2019
Event17th IEEE/ACM International Symposium on Code Generation and Optimization, CGO 2019 - Washington, United States
Duration: Feb 16 2019Feb 20 2019

Publication series

NameCGO 2019 - Proceedings of the 2019 IEEE/ACM International Symposium on Code Generation and Optimization

Conference

Conference17th IEEE/ACM International Symposium on Code Generation and Optimization, CGO 2019
CountryUnited States
CityWashington
Period2/16/192/20/19

Keywords

  • Code Generation
  • Code Optimization
  • Deep Learning
  • Distributed Systems
  • GPUs
  • Polyhedral Model
  • Tensors

ASJC Scopus subject areas

  • Software
  • Control and Optimization

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