7 citations · 19 across the 9 of their papers we have counts for
6 papers · 1 filter
LLM-HPC++: Evaluating LLM-Generated Modern C++ and MPI+OpenMP Codes for Scalable Mandelbrot Set Computation
Patrick Diehl, Noujoud Nader, Deepti Gupta
Parallel programming remains one of the most challenging aspects of High-Performance Computing (HPC), requiring deep knowledge of synchronization, communication, and memory models.…
Can LLMs Find Bugs in Code? An Evaluation from Beginner Errors to Security Vulnerabilities in Python and C++
Akshay Mhatre, Noujoud Nader, Patrick Diehl +1
Large Language Models (LLMs) such as ChatGPT-4, Claude 3, and LLaMA 4 are increasingly embedded in software/application development, supporting tasks from code generation to debugg…
LLM & HPC:Benchmarking DeepSeek's Performance in High-Performance Computing Tasks
Noujoud Nader, Patrick Diehl, Steve Brandt +1
Large Language Models (LLMs), such as GPT-4 and DeepSeek, have been applied to a wide range of domains in software engineering. However, their potential in the context of High-Perf…
Coupling approaches with non-matching grids for classical linear elasticity and bond-based peridynamic models in 1D
Patrick Diehl, Emily Downing, Autumn Edwards +1
Local-nonlocal coupling approaches provide a means to combine the computational efficiency of local models and the accuracy of nonlocal models. To facilitate the coupling of the tw…
LLM Benchmarking with LLaMA2: Evaluating Code Development Performance Across Multiple Programming Languages
Patrick Diehl, Nojoud Nader, Maxim Moraru +1
The rapid evolution of large language models (LLMs) has opened new possibilities for automating various tasks in software development. This paper evaluates the capabilities of the…
Closing a Source Complexity Gap between Chapel and HPX
Shreyas Atre, Chris Taylor, Patrick Diehl +1
A previous case study measured performance vs source-code complexity across multiple languages. The case study identified Chapel and HPX provide similar performance and code comple…