There is a growing interest in leveraging multiple large language models (LLMs) for automated code optimization. However, industrial platforms deploying multiple LLMs face a critical challenge: prompts optimized for one LLM often fail with others, requiring expensive model-specific prompt engineering. This cross-model prompt engineering bottleneck severely limits the practical deployment of multi-LLM systems in production environments. We introduce Meta-Prompted Code Optimization (Mpco), a framework that automatically generates high-quality, task-specific prompts across diverse LLMs while maintaining industrial efficiency requirements. Mpco leverages meta-prompting to dynamically synthesize context-aware optimization prompts by integrating project metadata, task requirements, and LLM-specific contexts. It is an essential part of the ARTEMIS code optimization platform for automated validation and scaling.Our comprehensive evaluation on five real-world codebases with 366 hours of runtime benchmarking demonstrates Mpco’s effectiveness: it achieves overall performance improvements up to 19.06% with the best statistical rank across all systems compared to baseline methods. Analysis shows that 96% of the top-performing optimizations stem from meaningful edits. Through systematic ablation studies and meta-prompter sensitivity analysis, we identify that comprehensive context integration is essential for effective meta-prompting and that major LLMs can serve effectively as meta-prompters, providing actionable insights for industrial practitioners.

Software developers sometimes use inefficient data structures or library interfaces without considering the potential impact they may have during the runtime of a program. This is due to the significant effort required to research and evaluate possibly more efficient alternatives. Consequently, there is a need for tooling to automate the design space exploration. Our proposed code optimisation solution, called Artemis++, tries to address this issue with automatic exploration and transformation of data structures to optimise software performance. In preliminary testing on three mainstream C++ libraries, we have observed improvements up to 16.09%, 27.90%, and 2.74% for CPU usage, runtime and memory, respectively.

Data structure selection and tuning is laborious but can vastly improve an application’s performance and memory footprint. Some data structures share a common interface and enjoy multiple implementations. We call them Darwinian Data Structures (DDS), since we can subject their implementations to survival of the fittest. We introduce ARTEMIS a multi-objective, cloud-based search-based optimisation framework that automatically finds optimal, tuned DDS modulo a test suite, then changes an application to use that DDS. ARTEMIS achieves substantial performance improvements for every project in 5 Java projects from DaCapo benchmark, 8 popular projects and 30 uniformly sampled projects from GitHub. For execution time, CPU usage, and memory consumption, ARTEMIS finds at least one solution that improves all measures for 86% (37/43) of the projects. The median improvement across the best solutions is 4.8%, 10.1%, 5.1% for runtime, memory and CPU usage.

Unisuite is an integrated suite of applications for the creation and management of e-learning. It consists of three state-of-the-art stand-alone entities, Unibook, Uniboard and UniPM, which communicate via smart interfaces. In this paper, the techntechnological and operational innovationsical characteristics of Unisuite are presented. The technological and operational innovations are analyzed and potential extensions and improvements are presented.