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Found 5 papers, 0 papers with code
The Landscape and Challenges of HPC Research and LLMs
Recently, language models (LMs), especially large language models (LLMs), have revolutionized the field of deep learning.
Performance Optimization using Multimodal Modeling and Heterogeneous GNN
There is, thus, a need for a general purpose and efficient tuning approach that can be easily scaled and adapted to various tuning tasks.
ParaGraph: Weighted Graph Representation for Performance Optimization of HPC Kernels
However, even with OpenMP, the developer must choose from among many strategies for exploiting a GPU or a CPU.
Power Constrained Autotuning using Graph Neural Networks
Our approach identifies OpenMP configurations at different power constraints that yield a geometric mean performance improvement of more than $25\%$ and $13\%$ over the default OpenMP configuration on a 32-core Skylake and a $16$-core Haswell processor respectively.
Learning Intermediate Representations using Graph Neural Networks for NUMA and Prefetchers Optimization
This paper demonstrates how the static Intermediate Representation (IR) of the code can guide NUMA/prefetcher optimizations without the prohibitive cost of performance profiling.
