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Found 4 papers, 1 papers with code
Performance-Aligned LLMs for Generating Fast Code
Optimizing scientific software is a difficult task because codebases are often large and complex, and performance can depend upon several factors including the algorithm, its implementation, and hardware among others.
Modeling Parallel Programs using Large Language Models
In this paper, we show how large language models (LLMs) can be applied to tasks specific to high performance and scientific codes.
Machine Learning-Driven Adaptive OpenMP For Portable Performance on Heterogeneous Systems
To address those challenges, this paper proposes new extensions to OpenMP for autonomous, machine learning-driven adaptation.
Scalable Comparative Visualization of Ensembles of Call Graphs
Optimizing the performance of large-scale parallel codes is critical for efficient utilization of computing resources.
Distributed, Parallel, and Cluster Computing Performance
