arXiv:2404.16077v4 Announce Type: replace-cross Abstract: Effective code optimization in compilers is crucial for computer and software engineering. The success of these optimizations primarily depends on the selection and ordering of the optimization passes applied to the code. While most compilers rely on a fixed sequence of optimization passes, current methods to find the optimal sequence either employ impractically slow search algorithms or learning methods that struggle to generalize to code unseen during training. We introduce CompilerDream, a model-based reinforcement learning approach
The increasing complexity of software and the computational demands of AI models are driving the need for more efficient and generalizable code optimization methods, pushing research into AI-driven compilers.
Optimizing compilers are critical infrastructure for computing performance, and AI-driven approaches have the potential to significantly enhance software efficiency across all domains, from cloud computing to edge devices.
This research introduces a machine learning approach that can learn to optimize code more effectively and generalize better than traditional methods, potentially leading to more performant software with less manual effort.
- · Software Developers
- · Cloud Computing Providers
- · AI/ML Hardware Designers
- · Compiler Developers
- · Legacy Compiler Optimization Teams
- · Companies reliant on highly specialized manual optimization
Increased software performance across a wide range of applications will be observed.
This could accelerate both AI model training and inference capabilities, further driving AI adoption.
More efficient software infrastructure could reduce overall compute energy consumption, contributing to sustainability goals.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG