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20242026
most citedCan Large Language Models Write Parallel Code?

30 citations · 33 across the 14 of their papers we have counts for

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5 papers · 1 filter

cs.CE2026

Report of the 2026 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science

Lois Curfman McInnes, Dorian Arnold, Prasanna Balaprakash +45

Scientific computing is undergoing rapid transformation as advances in artificial intelligence, heterogeneous computing, automation, and data-intensive research reshape not only co…

cs.PF2026

KEET: Explaining Performance of GPU Kernels Using LLM Agents

Joshua H. Davis, Klaudiusz Rydzy, Srinivasan Ramesh +5

Performance profiles of GPU kernels generated by tools such as Nsight Compute are rich in detail but are often challenging to interpret. To achieve the best performance possible on…

cs.LG2026

LongCoT: Benchmarking Long-Horizon Chain-of-Thought Reasoning

Sumeet Ramesh Motwani, Daniel Nichols, Charles London +17

As language models are increasingly deployed for complex autonomous tasks, their ability to reason accurately over longer horizons becomes critical. An essential component of this…

cs.DC2026

Record-Remix-Replay: Hierarchical GPU Kernel Optimization using Evolutionary Search

Daniel Nichols, Konstantinos Parasyris, Caetano Melone +3

As high-performance computing and AI workloads become increasingly dependent on GPUs, maintaining high performance across rapidly evolving hardware generations has become a major c…

cs.LG2026

Communication-free Sampling and 4D Hybrid Parallelism for Scalable Mini-batch GNN Training

Cunyang Wei, Siddharth Singh, Aishwarya Sarkar +7

Graph neural networks (GNNs) are widely used for learning on graph datasets derived from various real-world scenarios. Learning from extremely large graphs requires distributed tra…