Since the mid-1990s, researchers have been trying to use machine-learning-based
approaches to solve a number of different compiler optimization problems. These …

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 …

Heterogeneous multiprocessors are increasingly important in the multi-core era due to their
potential for high performance and energy efficiency. In order for software to fully realize this …

To meet the demands of modern architectures, optimizing compilers must incorporate an
ever larger number of increasingly complex transformation algorithms. Since code …

Heterogeneous architectures are currently widespread. With the advent of easy-to-program
general purpose GPUs, virtually every recent desktop computer is a heterogeneous system …

Performance tuning is an important and time consuming task which may have to be
repeated for each new application and platform. Although iterative optimisation can …

We present a plan to add loop nest optimizations in GCC based on polyhedral
representations of loop nests. We advocate a static analysis approach based on a hierarchy …

Modern compiler optimization is a complex process that offers no guarantees to deliver the
fastest, most efficient target code. For this reason, compilers struggle to produce a stable …

Large Language Models (LLMs) have demonstrated remarkable capabilities across a
variety of software engineering and coding tasks. However, their application in the domain of …

Despite ceaseless efforts, extremely large and complex optimization space makes even the
state-of-the-art compilers fail in delivering the most performant setting that can fully utilize the …