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 8, 2017

The paper, "Autotuning GPU Kernels via Static and Predictive Analysis " by Robert Lim, Boyana Norris, and Allen Malony was accepted to ICPP.

April, 2017

Kanika Sood received a Student Travel Award to attend the PETSc 2017 User Meeting to be held on 6/14-6/17/2017 where she will be presenting her work on Comparative Performance Modeling of Parallel Preconditioned Krylov Methods.

Summer, 2017

Several HPCL members are going to national labs for summer research internships: Kanika Sood and Shweta Gupta are going to Argonne National Lab, Sam Pollard and Kewen Meng are going to Lawrence Livermore National Lab.

February, 2017

Kanika Sood received a SIAM Student Travel Award to attend the SIAM Conference on Computational Science and Engineering (CSE17) held 2/27-3/3/2017 where she presented her work on Lighthouse in the Minisymposterium on Software Productivity and Sustainability for CSE and Data Science. Sara Riazi presented her work on integrating the Galaxy workflow system with Spark at the same minisymposium.

November 10, 2016

Anu Deodhar received an UROP Mini-grant Award (UO Research and Inovation) in support of her undergraduate Honors Thesis work on ``Urban Noise Mapping at University of Oregon''.

November 6, 2016

The paper by Sara Riazi and Boyana Norris, ``GraphFlow: Workflow-based Big Graph Processing'' has been accepted for publication at the Third International Workshop on High Performance Big Graph Data Management, Analysis, and Mining (BigGraphs 2016).

November 6, 2016

The paper by T. J. LaGrow, Jacob Bieker and Boyana Norris, ``Do You Know Where Your Research Is Being Used? An Exploration of scientific literature using Natural Language Processing" has been accepted for publication in the Oregon Undergraduate Research Journal.

October 25, 2016

Kanika Sood and Sara Riazi will be presenting at the SIAM Conference on Computational Science and Engineering (CSE17), Atlanta, GA in February, 2017. Kanika also received a travel scholarship for the conference.

September, 2016

Boyana will serve as the area chair of the Programming Systems Technical Program area of Supercomputing 2017.

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.

  • - Extract the class relationships (inheritence and containment) from C++ software [330, using/writing parsers]
  • - Ongoing performance measurement and data analysis of HPC applications [working in a Unix environment]
  • - 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 [some 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.