# Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code

arXiv: https://arxiv.org/abs/1804.10694 · HTML: https://mlsanity.com/paper/1804.10694/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2018.

## Built on by (most cited first, 30 of 53)

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- [A Reinforcement Learning Environment for Polyhedral Optimizations](https://mlsanity.com/paper/2104.13732/) (2021)
- [Bring Your Own Codegen to Deep Learning Compiler](https://mlsanity.com/paper/2105.03215/) (2021)
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- [A Reinforcement Learning Environment for Automatic Code Optimization in the MLIR Compiler](https://mlsanity.com/paper/2409.11068/) (2024)
- [PyGraph: Robust Compiler Support for CUDA Graphs in PyTorch](https://mlsanity.com/paper/2503.19779/) (2025)
- [REASONING COMPILER: LLM-Guided Optimizations for Efficient Model Serving](https://mlsanity.com/paper/2506.01374/) (2025)
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- [Collage: Seamless Integration of Deep Learning Backends with Automatic Placement](https://mlsanity.com/paper/2111.00655/) (2021)
- [Demystifying Map Space Exploration for NPUs](https://mlsanity.com/paper/2210.03731/) (2022)
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- [OLLIE: Derivation-based Tensor Program Optimizer](https://mlsanity.com/paper/2208.02025/) (2022)

## Abstract (arXiv)

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 extensions 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.