What is HPC?

The High-Performance Computing (HPC) Laboratory is a part of the Department of Computer and Information Science at the University of Oregon. The High Performance Computing Laboratory directed by Prof. Boyana Norris conducts research in several areas of high-performance computing (HPC), including static analysis of software for building performance models and detecting security vulnerabilities, source-to-source approaches for semantics-preserving (e.g., performance optimization) and semantics-modifying (e.g., security vulnerability fixes, automatic differentiation) transformations. The HPC Lab also performs research in modeling runtime characteristics of software, and developing and employing numerical optimization techniques for maximizing multiple runtime objectives (performance, energy efficiency, resilience, etc.).

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

News

May 20, 2019

Dr. Kanika Sood successfully defended her dissertation!

May 18, 2019

The paper Performance Analysis of Compressed Batch Matrix Operations on Small Matrices by Brian Gravelle and Boyana Norris was accepted to HPCS.

May 17, 2019

Samuel Pollard received the University of Oregon's general university scholarship for the 2019-2020 academic year.

Summer 2019

Several students earned internships this summer including Samuel Pollard at Sandia Labs in Livermore, CA, Brian Gravelle in Los Alamos Labs in NM, and Kewen Meng at Databricks.

Mar 13, 2019

Brian Gravelle passed his area exam and advanced to candidacy.

Mar 3, 2019

Samuel Pollard was awarded a travel grant to attend the International Conference on Performance Engineering in Mumbai, India.

Feb 26, 2019

Samuel Pollard presented the talk Reproducibility in Parallel Graph Algorithms. SIAM CSE 2019, Spokane, WA. [URL][Slides]

Feb 21, 2019

Samuel Pollard passed his area exam and advanced to candidacy.

Feb 11, 2019

The work-in-progress paper "A Performance and Recommendation System for Parallel Graph Processing Implementations" by Samuel Pollard, Sudharshan Srinivasan, and Boyana Norris was accepted for publication at ICPE 2019. [PDF][Bib]

More news can be found at our news archive.

Open projects

Undergraduate / short-term graduate: these are term-long projects achiavable 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), National Science Foundation (NSF), and RNET Technologies, Inc (Dayton, OH).

Relevant conferences and workshops.