# LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

arXiv: https://arxiv.org/abs/2403.11522 · HTML: https://mlsanity.com/paper/2403.11522/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 50 references that are in the map.
- First posted on arXiv 18 Mar 2024.

## Builds on (most cited first, 8 of 8)

- [Ansor: Generating High-Performance Tensor Programs for Deep Learning](https://mlsanity.com/paper/2006.06762/) (2020)
- [Learning to Optimize Tensor Programs](https://mlsanity.com/paper/1805.08166/) (2018)
- [Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions](https://mlsanity.com/paper/1802.04730/) (2018)
- [Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code](https://mlsanity.com/paper/1804.10694/) (2018)
- [Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks](https://mlsanity.com/paper/1808.07412/) (2018)
- [A Deep Learning Based Cost Model for Automatic Code Optimization](https://mlsanity.com/paper/2104.04955/) (2021)
- [TIRAMISU: A Polyhedral Compiler for Dense and Sparse Deep Learning](https://mlsanity.com/paper/2005.04091/) (2020)
- [PENCIL: Towards a Platform-Neutral Compute Intermediate Language for DSLs](https://mlsanity.com/paper/1302.5586/) (2013)

## Built on by (most cited first, 7 of 7)

- [Agentic Auto-Scheduling: An Experimental Study of LLM-Guided Loop Optimization](https://mlsanity.com/paper/2511.00592/) (2025)
- [A Reinforcement Learning Environment for Automatic Code Optimization in the MLIR Compiler](https://mlsanity.com/paper/2409.11068/) (2024)
- [LOOPerSet: A Large-Scale Dataset for Data-Driven Polyhedral Compiler Optimization](https://mlsanity.com/paper/2510.10209/) (2025)
- [CHEHAB RL: Learning to Optimize Fully Homomorphic Encryption Computations](https://mlsanity.com/paper/2601.19367/) (2026)
- [Data-efficient Performance Modeling via Pre-training](https://mlsanity.com/paper/2501.14438/) (2025)
- [A Deep Learning Model for Predicting Transformation Legality](https://mlsanity.com/paper/2511.06120/) (2025)
- [ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation](https://mlsanity.com/paper/2601.04327/) (2026)

## Abstract (arXiv)

While polyhedral compilers have shown success in implementing advanced code transformations, they still face challenges in selecting the ones that lead to the most profitable speedups. This has motivated the use of machine learning based cost models to guide the search for polyhedral optimizations. State-of-the-art polyhedral compilers have demonstrated a viable proof-of-concept of such an approach. While promising, this approach still faces significant limitations. State-of-the-art polyhedral compilers that use a deep learning cost model only support a small subset of affine transformations, limiting their ability to explore complex code transformations. Furthermore, their applicability does not scale beyond simple programs, thus excluding many program classes from their scope, such as those with non-rectangular iteration domains or multiple loop nests. These limitations significantly impact the generality of such compilers and autoschedulers and put into question the whole approach. In this paper, we introduce LOOPer, the first polyhedral autoscheduler that uses a deep learning based cost model and covers a large space of affine transformations and programs. LOOPer allows the optimization of an extensive set of programs while being effective at applying complex sequences of polyhedral transformations. We implement and evaluate LOOPer and show that it achieves competitive speedups over the state-of-the-art. On the PolyBench benchmarks, LOOPer achieves a geometric mean speedup of 1.84x over Tiramisu and 1.42x over Pluto, two state-of-the-art polyhedral autoschedulers.