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Kevin A. Huck

Faculty Research Associate
Oregon Advanced Computing Institute
for Science and Society (OACISS),
University of Oregon

  Email:
CV: PDF
Publications: List in Google Scholar (Contact me for PDF copies)
ORCID: 0000-0001-7064-8417
GitHub: Kevin Huck on GitHub
LinkedIn: Kevin Huck on LinkedIn
 
Current Research Projects:
  • RAPIDS2: SciDAC Institute for Resource and Application Productivity through computation, Information, and Data Science
  • OASIS: Organization for the Advancement and Stewardship of Integrated Software
 
Prior Research Projects:
  • ECP PROTEAS-TUNE: Programming Toolchain for Emerging Architectures and Systems
  • ECP CODAR: Co-Design Center For Online Data Analysis and Reduction at Exascale
  • ECP WDMAPP: High-fidelity Whole Device Modeling of Magnetically Confined Fusion Plasmas
  • RAPIDS: SciDAC Institute for Computer Science and Data
  • PHYLANX: A Distributed Array Toolkit
  • SUPER: SciDAC Institute for Sustained Performance, Energy, and Resilience
  • GlassBox: SI2-SSI: Collaborative Research: A Glass Box Approach to Enabling Open, Deep Interactions in the HPC Toolchain
  • XPRESS: eXascale PRogramming Environment and System Software
  • Eclipse PTP: A Productive and Accessible Development Workbench for HPC Applications Using the Eclipse Parallel Tools Platform
  • PERI: SciDAC Performance Engineering Research Institute

Software Projects:

Research Interests:
  • Parallel performance measurement and analysis, particularly in the area of scientific applications on large scale, high performance computing (HPC) systems.
  • Runtime introspection and autonomic runtime adaptation for improved performance and power/energy efficiency.
  • User space monitoring of HPC simulations to validate user configuration settings and eliminate common misconfigurations.
  • Software tool design, library integration, and software engineering.
 
Education:

Current & Former
Employers & Clients:

Useful Links:
TAU data rendered by Ascent during 64 process run of LULESH
Figure: LULESH 2.0.3 executed with 64 MPI ranks, measured by TAU. Time spent in main loop rendered every 100 timesteps by Alpine-Ascent. Clockwise from upper left: Computed energy, accumulated time in main loop, time in main loop during last 100 timesteps, Δ time in main loop from previous 100 time steps.

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