构建2800万条数据的编译器优化数据集，加速学习型编译优化研究。
LOOPerSet: A Large-Scale Dataset for Data-Driven Polyhedral Compiler Optimization
- 基于22万条合成程序生成2800万条带性能标签的数据点。
- 每条数据关联程序与多步保语义变换后的执行时间。
- 开源可用，适合训练和评测编译优化模型的研究者使用。
机器学习在编译优化领域，尤其是多面体模型中的进展，受限于大规模公开性能数据集的缺乏。这一数据瓶颈迫使研究者耗费大量成本进行数据生成，阻碍了创新与可复现研究。为此，我们提出LOOPerSet，一个包含2800万条标注数据点的公开数据集，源自22万条唯一、合成生成的多面体程序。每个数据点将一个程序及其一系列保语义变换（如融合、倾斜、分块、并行化）映射到真实执行时间。其规模与多样性使其成为训练与评估学习型代价模型、测试新模型架构及探索自动化多面体调度前沿的重要资源。数据集采用宽松许可协议发布，旨在促进可复现研究，降低数据驱动编译优化的研究门槛。
原文摘要 · Abstract (English)
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.
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