Parallel, Distributed, and High Performance Computing

Research in parallel, distributed, and high-performance computing centers around many fundamental questions.  How can we most effectively use novel and unique architectures?  What's the programmers view of the machine?  How can we develop programs that execute efficiently on a wide variety of high-performance architectures?   How do we find bugs in high-performance programs?

At the University of Arizona, our research in this area revolves around fundamental questions that center around one goal: that users of high-performance computing systems can write and visualize programs easily and still have these programs execute efficiently.  Our work ranges from finding new paradigms for expressing high-performance computing applications in high-level languages (Strout), to understanding aspects of the application when it executes, via visualization (Isaacs), to building models and systems that ensure the efficient execution of these programs (Lowenthal).  While our research emphasizes fundamental concepts, for our research to be useful to the broader scientific community we implement our techniques within new or existing high-performance computing systems.

Our group has many connections to real-world high-performance computing.  We have collaborated with nearly every national laboratory, with a focus on Lawrence Livermore and Argonne.  In addition, we partner with colleagues from around the UA campus to ensure that we build tools and systems that are useful to UA faculty who work in other disciplines.

PDHPC Faculty

David Lowenthal

Professor Office: GS 705 Interests: Parallel and distributed computing, operating systems, and run-time systems. (Ph.D., The University of Arizona, 1996)

PhD Students

H. M. Abdul Fattah

PhD Student Office: GS 710 Interests: Natural Language Processing, Question-Answering, Data Mining, Machine Learning Advisor: Dr. Eduardo Blanco

Brandon Neth

PhD Student Office: GS 749 Interests: Programming Languages, Compilers and High Performance Computing Advisor: Dr. Michelle Strout

Sayef Azad Sakin

PhD Student Office: GS 756 Interests: Data Visualization and High Performance Computing Advisor: Dr. Kate Isaacs

Connor Scully-Allison

PhD Student Office: N/A Interests: Data Visualization, Data Management and Interdisciplinary Software Engineering Advisor: Dr. Kate Isaacs

Shreya Nupur Shakya

PhD Student Office: GS 756 Interests: Data visualization and Graphics Advisor: TBD