3 citations · 3 across the 6 of their papers we have counts for
6 papers
Effect of Abstractions and Prompting Strategies on LLM-Guided High-Performance Optimizations
Jiří Klepl, Matyáš Brabec, Martin Kruliš
Code performance optimization is a vital aspect of modern software development, as it enables faster response times and reduced resource usage. These optimizations require a deep u…
Tutoring LLM into a Better CUDA Optimizer
Matyáš Brabec, Jiří Klepl, Michal Töpfer +1
Recent leaps in large language models (LLMs) caused a revolution in programming tools (like GitHub Copilot) that can help with code generation, debugging, and even performance opti…
Ensemble-based modeling abstractions for modern self-optimizing systems
Michal Töpfer, Milad Abdullah, Tomáš Bureš +2
In this paper, we extend our ensemble-based component model DEECo with the capability to use machine-learning and optimization heuristics in establishing and reconfiguration of aut…
Online ML Self-adaptation in Face of Traps
Michal Töpfer, František Plášil, Tomáš Bureš +3
Online machine learning (ML) is often used in self-adaptive systems to strengthen the adaptation mechanism and improve the system utility. Despite such benefits, applying online ML…
Scalable semi-supervised dimensionality reduction with GPU-accelerated EmbedSOM
Adam Šmelko, Soňa Molnárová, Miroslav Kratochvíl +4
Dimensionality reduction methods have found vast application as visualization tools in diverse areas of science. Although many different methods exist, their performance is often i…
Towards fuzzification of adaptation rules in self-adaptive architectures
Tomáš Bureš, Petr Hnětynka, Martin Kruliš +5
In this paper, we focus on exploiting neural networks for the analysis and planning stage in self-adaptive architectures. The studied motivating cases in the paper involve existing…