Description

The High Performance Computing Research group includes three faculty members with research interests in parallelization, embedded computing, compilers, and scientific computing. Some of the specific problems we actively study include mapping stream-like computations to embedded processors, scaling regular and irregular computations to large numbers of processors, and making program analysis infrastructure available to any compiler infrastructure.

Participating Faculty

Dr. Wim Bohm, Image processing on FPGAs, high-level programming of high performance embedded computing systems, compilers, and parallel computing.

Dr. Sanjay Rajopadhye, Systolic arrays, embedded systems, (co) design of VLSI systems, functional programming and formal methods, computer architecture, mapping of algorithms to architectures, automatic parallelization, tiling and parallel algorithms/architectures (notably for the knapsack problem).

Dr. Michelle Strout, Compilers, scientific computing, domain-specific static and dynamic program analysis, automatic parallelization, and tiling.
Dr. Shrideep Pallickara: cloud computing runtimes, scientific file systems, and content distribution networks.

Current Research

The following list is a sample of the research currently being conducted by the high performance computing group. Visit the participating faculty's individual web pages using links in the above list to learn more.

Embedded Computing

HiPHiPECS

Cameron



Automatic Parallelization and Tiling

The Polyhedral model

Parallelization of stencil computations

PIES: Parallelization using Inspector/Executor Strategies



Compilers: Code Generation and Program Analysis

The Alpha Programming Language

The OpenAnalysis Toolkit



Cloud Computing

The Granules runtime for cloud computing



Scientific Computing

SAXS: Small Angle Scattering

Curriculum

• CS451: Operating Systems
  
  
• CS 453: Introduction to Compiler Construction
  
  
• CS 460: Embedded Systems
  
  
• CS 475: Parallel Programming
  
  
• CS 553: Algorithmic Language Compilers
  
  
• CS 555: Distributed Systems
  
  
• CS 560: Reconfigurable Computing
  
  
• CS 575: Parallel Processing
  
  
• CS 653: Topic in Programming Language Implementation

Additional courses focussed in various high performance computation topics may also be offered.

Information on the Graduate Program in Computer Science at Colorado State University can be obtained from: gradinfo@cs.colostate.edu.

Contact Information

Requests for additional information can or for copies of papers may be sent to any of the participating faculty. E-mail addresses are listed on the faculty web pages.

Computer Science Department Home Page