# LOOPerSet: A Large-Scale Dataset for Data-Driven Polyhedral Compiler Optimization

arXiv: https://arxiv.org/abs/2510.10209 · HTML: https://mlsanity.com/paper/2510.10209/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 11 Oct 2025.

## Builds on (most cited first, 6 of 6)

- [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)
- [Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code](https://mlsanity.com/paper/1804.10694/) (2018)
- [A Deep Learning Based Cost Model for Automatic Code Optimization](https://mlsanity.com/paper/2104.04955/) (2021)
- [TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs](https://mlsanity.com/paper/2308.13490/) (2023)
- [LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers](https://mlsanity.com/paper/2403.11522/) (2024)

## Built on by (most cited first, 1 of 1)

- [Step-TP: A Grounded, Step-Level Dataset with Chain-of-Thought Reasoning for LLM-Guided Tensor Program Optimization](https://mlsanity.com/paper/2605.25954/) (2026)

## Abstract (arXiv)

The advancement of machine learning for compiler optimization, particularly within the polyhedral model, is constrained by the scarcity of large-scale, public performance datasets. This data bottleneck forces researchers to undertake costly data generation campaigns, slowing down innovation and hindering reproducible research learned code optimization. To address this gap, we introduce LOOPerSet, a new public dataset containing 28 million labeled data points derived from 220,000 unique, synthetically generated polyhedral programs. Each data point maps a program and a complex sequence of semantics-preserving transformations (such as fusion, skewing, tiling, and parallelism)to a ground truth performance measurement (execution time). The scale and diversity of LOOPerSet make it a valuable resource for training and evaluating learned cost models, benchmarking new model architectures, and exploring the frontiers of automated polyhedral scheduling. The dataset is released under a permissive license to foster reproducible research and lower the barrier to entry for data-driven compiler optimization.