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.).

Current active projects can be found here while general research areas are listed in the toolbar on the left.

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


Summer 2020

Several students are doing research internships: Brian Gravelle at Los Alamos National Labs and Sudharshan Srinivasan at Argonne National Labs.

June 4, 2020

Samuel Pollard was awarded the General University Scholarship from the University of Oregon.

September 13, 2019

Samuel Pollard presented his binary analysis work Quameleon at the first workshop on instruction set architecture specification, SpISA.

August, 2019

Dr. Sarai Riazi successfully defended her dissertation!

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.

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), National Science Foundation (NSF), and RNET Technologies, Inc (Dayton, OH).

Relevant conferences and workshops.