Recent efforts applying machine learning techniques to query optimization have shown few
practical gains due to substantive training overhead, inability to adapt to changes, and poor …

High-performance tensor programs are crucial to guarantee efficient execution of deep
neural networks. However, obtaining performant tensor programs for different operators on …

Abstract Machine learning (ML) is increasingly seen as a viable approach for building
compiler optimization heuristics, but many ML methods cannot replicate even the simplest of …

We explore the novel application of Large Language Models to code optimization. We
present a 7B-parameter transformer model trained from scratch to optimize LLVM assembly …

Interest in applying Artificial Intelligence (AI) techniques to compiler optimizations is
increasing rapidly, but compiler research has a high entry barrier. Unlike in other domains …

The increasing complexity of computing systems places a tremendous burden on optimizing
compilers, requiring ever more accurate and aggressive optimizations. Machine learning …

We propose IR2Vec, a Concise and Scalable encoding infrastructure to represent programs
as a distributed embedding in continuous space. This distributed embedding is obtained by …

Leveraging machine-learning (ML) techniques for compiler optimizations has been widely
studied and explored in academia. However, the adoption of ML in general-purpose …

Fueled by recent accomplishments in quantum computing hardware and software, an
increasing number of problems from various application domains are being explored as …

Writing optimising compilers is difficult. The range of programs that may be presented to the
compiler is huge and the systems on which they run are complex, heterogeneous, non …