Learning to superoptimize programs - Workshop Version

2016-12-04 · Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H. S. Torr, Pushmeet Kohli

Superoptimization requires the estimation of the best program for a given computational task. In order to deal with large programs, superoptimization techniques perform a stochastic search. This involves proposing a modification of the current program, which is accepted or rejected based on the improvement achieved. The state of the art method uses uniform proposal distributions, which fails to exploit the problem structure to the fullest. To alleviate this deficiency, we learn a proposal distribution over possible modifications using Reinforcement Learning. We provide convincing results on the superoptimization of "Hacker's Delight" programs.

📄 PDF Abstract BibTeX arXiv:1612.01094

Code (0)

등록된 구현이 없습니다.

Tasks

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Learning to Superoptimize Real-world Programs

2021-09-28 · Alex Shypula, Pengcheng Yin, Jeremy Lacomis, Claire Le Goues 외

Program optimization is the process of modifying software to execute more efficiently. Superoptimizers attempt to find the optimal program by employing significantly more expensive search and constraint solving technique…

Compiler Optimization Imitation Learning

Axon: A Synthesizing Superoptimizer for Tensor Programs

2026-06-24 · Akash Kothari, Shaowei Zhu, Daniel Kroening, Chungha Sung arxiv

Writing high performance kernels for AI accelerators requires deep expertise in tiling, instruction selection, data layout, and operator fusion placing a significant burden on programmers. In this paper, we focus on tile…

Program Synthesis

Mirage: A Multi-Level Superoptimizer for Tensor Programs

2024-05-09 · Mengdi Wu, Xinhao Cheng, Shengyu Liu, Chunan Shi 외

We introduce Mirage, the first multi-level superoptimizer for tensor programs. A key idea in Mirage is $\mu$Graphs, a uniform representation of tensor programs at the kernel, thread block, and thread levels of the GPU co…

GPU Navigate

Prism: Symbolic Superoptimization of Tensor Programs

2026-04-16 · Mengdi Wu, Xiaoyu Jiang, Oded Padon, Zhihao Jia arxiv

This paper presents Prism, the first symbolic superoptimizer for tensor programs. The key idea is sGraph, a symbolic, hierarchical representation that compactly encodes large classes of tensor programs by symbolically re…

Graph Generation