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.


May 2021

Kewen Meng successfully defended his dissertation!

Samuel Pollard successfully defended his dissertation! His talk can be found here.

Paper accepted: S. Hussain, K. Chicoine, and B. Norris. Empirical investigation of code quality rule violations in HPC applications. To appear in Proceedings of the 2nd International DevOps Quality Management Workshop, 2021.

April 2021

Journal paper accepted: A. Khanda, S. Srinivasan, S. Bhowmick, B. Norris, and S. K. Das. A parallel algorithm template for updating single-source shortest paths in large-scale dynamic networks. To appear in IEEE TPDS Special Section on Innovative R&D toward the Exascale Era, 2021.

February 2021

Several HPCL students will spend summer doing research at various national labs, including Argonne, Sandia, Livermore, and Los Alamos.

November 11, 2020

Samuel Pollard presented his and Boyana's paper, A Statistical Analysis of Error in MPI Reduction Operations at the Correctness workshop at Supercomputing.

September 2020

HPCL welcomes two new graduate students to our lab: Dewi Yokelson and Parsa Baghery.

Paper published: S. Lantz, K. McDermott, M. Reid, D. Riley, P. Wittich, S. Berkman, G. Cerati, M. Kortelainen, A. R. Hall, P. Elmer, B. Wang, L. Giannini, V. Krutelyov, M. Masciovecchio, M. Tadel, F. Würth- wein, A. Yagil, B. Gravelle, and B. Norris. Speeding up particle track reconstruction using a paral- lel kalman filter algorithm. Journal of Instrumentation 15(09):P09030–P09030, sep 2020, https: //

Kewen Meng and Boyana Norris's short paper on ``Guiding Code Optimizations with Deep Learning-Based Code Matching'' was accepted at the 33rd Workshop on Languages and Compilers for Parallel Computing (LCPC 2020).

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.