The High-Performance Computing1 Laboratory (HPCL) is part of the Department of Computer and Information Science at the University of Oregon.

In HPCL, we believe that achieving high performance must not come at the cost of software quality, maintainability, and extensibility. Moreover, we believe that the productivity and happiness of HPC software developers is important to the overall success of HPC in enabling more and better science across many scientific domains.

HPCL is directed by Prof. Boyana Norris and conducts research in several areas, including optimizing compilers, performance modeling and optimization, parallel algorithms, and software engineering. Example projects include static and dynamic analysis of software for building application performance models, ensuring software quality, or detecting security vulnerabilities; using machine learning and other approaches to model run-time characteristics of software; developing data mining techniques to study and improve HPC software engineering processes; applying natural language processing methods to study and improve HPC software developer productivity; designing new algorithms or improving existing ones in several application areas, including large-scale dynamic graphs, computational physics, and computational biology.

1What is HPC?

Short-term research projects are available for advanced undergrads or MS students.

News

May 2023

Paper accepted: D. Yokelson, M. R. J. Charest, Y. W. Li. HPC Application Performance Prediction with Machine Learning on New Architectures. PERMAVOST Workshop 2023 at HPDC, Orlando, Florida.

March 2023

Paper accepted: H. Ather, J. L. Bez, B. Norris, S. Byna. Illuminating the I/O Optimization Path of Scientific Applications. ISC High Performance 2023, Hamburg, Germany.

February 2023

Extended abstract accepted: D. Yokelson, S. Ramesh, A. Malony, B. Norris, O. Lappi, M. Vaisala, T. Puro, M. Korpi-Lagg, K. Heljanko. Observability, Monitoring, and In Situ Analytics in Exascale Applications. Full paper to be presented at Cray User Group, May 2023, Helsinki, Finland.

January 2023

Sam Mergendahl joins the the High-Performance Computing Lab!

December 2022

Hammad Ather successfully passed his directed research project!

November 2022

Paper published: J. L. Bez, H. Ather and S. Byna. Drishti: Guiding End-Users in the I/O Optimization Journey. 2022 IEEE/ACM International Parallel Data Systems Workshop (PDSW), Dallas, TX, USA, 2022.

HPCL students Dewi Yokelson, Hammad Ather, and Aliza Lisan volunteer at Supercomputing in Dallas, TX.

Former HPCL lab members Brian Gravelle and Samuel Pollard present their research at Supercomputing in Dallas, TX.

June 2022

Aliza Lisan has been awarded a General University Scholarship!

Dewi Yokelson successfully passed her area exam and has advanced to candidacy!

May 2022

Brian Gravelle successfully defended his dissertation!

April 2022

Paper published: D. Yokelson, N.V. Tkachenko, B. Robey, Y.W. Li, and P. Dub. Performance Analysis of CP2K Code for Ab Initio Molecular Dynamics on CPUs and GPUs. Journal of Chemical Information and Modeling, April 22, 2022.

HPCL students have accepted summer internships at national labs, including Lawrence Berkeley National Laboratory, Lawrence Livermore National Laboratory, and Pacific Northwest National Laboratory.

January 2022

Sudharshan Srinivasan begins an internship at Advanced Micro Devices (AMD), and Dewi Yokelson begins graduate research work at Los Alamos National Laboratory.

More news can be found at our news archive.

Open projects

Undergraduate / short-term graduate: these are term-long projects achieavable with up to 10-15 hours per week effort. Experience or background that may be helpful is listed in square brackets. Interested students should contact Prof. Norris.

  • - Software development practices analysis through revision control data mining and natural language processing
  • - Performance analysis and optimization of scientific codes (usually these are parallel applications using MPI, OpenMP, or TBB). We typically have a number of scientific applications that we analyze and optimize. Some experience with performance analysis is helpful, but not required.
  • - Extract the class relationships (inheritence and containment) from C++ software [330, using/writing parsers]
  • - Using binary analysis to identify computational patterns and anti-patterns (for performance or power efficiency) [314, 429]
  • - Text analysis of selected portions of the scientific literature to discover and categorize use cases for scientific software [data mining]

The HPC Lab is generously supported by donations and grants from the Department of Energy (DOE) and the National Science Foundation (NSF).

Past sponsors include RNET Technologies, Inc (Dayton, OH) and Paratools, Inc..

Relevant conferences and workshops.