SysLLMatic: Large Language Models are Software System Optimizers

Automatic software system optimization can improve software speed and save energy. Traditional approaches to optimization rely on manual tuning and compiler heuristics, limiting their ability to generalize across diverse codebases. Recent methods using LLMs introduce automation, but they do not scale effectively to the complexity and size of real-world software systems, leaving a gap in practical applicability. We present SysLLMatic, a system that integrates LLMs with performance diagnostics feedback and a curated catalog of 43 optimization patterns to automatically optimize software code. Our approach builds on recent advances in LLM-based code optimization and specifically targets the limitations of existing systems in handling real-world software applications. We evaluate it on three benchmark suites: HumanEval_CPP (competitive programming in C++), SciMark2 (scientific kernels in Java), and DaCapoBench (large-scale software systems in Java). Results show that SysLLMatic can improve software system performance, including latency, throughput, energy efficiency, memory usage, and CPU utilization. It consistently outperforms state-of-the-art LLM baselines on microbenchmarks. On large-scale application codes, to which prior LLM approaches have not scaled, it surpasses compiler optimizations, achieving average relative improvements of 1.5x in latency (vs. 1.01x for the compiler) and 1.76x in throughput (vs. 1.02x for the compiler). Our findings demonstrate that LLMs, guided by principled system thinking through the optimization pattern catalog and appropriate performance diagnostics, can serve as viable software system optimizers. We further identify limitations of our approach and the challenges involved in handling complex applications. This work provides a foundation for generating optimized code across various languages, benchmarks, and program sizes in a principled manner.