Matthew Leinhauser

Matthew Leinhauser
University of Delaware | UDel UD · Department of Computer and Information Sciences

Master of Science

About

7
Publications
1,433
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1
Citation
Introduction
Matthew Leinhauser currently is a Computer and Informations Sciences PhD candidate and Graduate Research Assistant at the University of Delaware. Matthew is interested in doing research in Machine Learning, Artificial Intelligence, and applying computer science to interdisciplinary aspects such as exercise science and communications.

Publications

Publications (7)
Article
Due to the recent announcement of the Frontier supercomputer, many scientific application developers are working to make their applications compatible with AMD (CPU-GPU) architectures, which means moving away from the traditional CPU and NVIDIA-GPU systems. Due to the current limitations of profiling tools for AMD GPUs, this shift leaves a void in...
Preprint
Full-text available
HPC systems employ a growing variety of compute accelerators with different architectures and from different vendors. Large scientific applications are required to run efficiently across these systems but need to retain a single code-base in order to not stifle development. Directive-based offloading programming models set out to provide the requir...
Preprint
Full-text available
Due to the recent announcement of the Frontier supercomputer, many scientific application developers are working to make their applications compatible with AMD architectures (CPU-GPU), which means moving away from the traditional CPU and NVIDIA-GPU systems. Due to the current limitations of profiling tools for AMD GPUs, this shift leaves a void in...
Technical Report
Full-text available
Three kernels, Current Deposition (also known as Compute Current), Particle Push (Move and Mark), and Shift Particles are known to be some of the most time-consuming kernels in PIConGPU. The Current Deposition kernel and Particle Push kernel both set up the particle attributes for running any physics simulation with PIConGPU, so it is crucial to im...
Technical Report
Full-text available
This is a technical report that summarizes findings on the analysis of PIConGPU's three most intensive kernels by using NVProf Profiler tool and Summit system at the Oak Ridge National Lab (ORML). The kernels, Current Deposition (also known as Compute Current), Particle Push (Move and Mark), and Shift Particles are known to be some of the biggest k...
Conference Paper
Full-text available
Instructions should help individuals carry out a task they need help with. In the domain of physical exercise, instructions are very useful in helping an individual execute a biomechanically correct movement and stay injury-free through that movement. If the instructions for that movement are ambiguous and non-descriptive, the risk of suffering inj...

Projects

Project (1)
Project
This is a prestigious software project, called Center for Accelerated Application Readiness (CAAR), awarded by ORNL in September 2019. Only 8 CAAR teams have been selected by ORNL across the United States to work on software stacks for one of the upcoming Exascale systems, Frontier to be housed at ORNL by 2021 timeframe. The CAAR project uses a Particle In Cell GPU (PIConGPU), an extremely scalable, heterogeneous, fully relativis- tic C++ code. PIConGPU is available as open-source project on GitHub, for plasma and laser-plasma physics used to develop advanced particle accelerators for radiation therapy of cancer, high energy physics and photon science. The code is suitable for production-quality runs on state-of-the-art supercomputers driven by manycore accelerators as well as traditional architectures with a single, maintainable code base.