High Performance Computing

Research in this area explores the development and analysis of parallel algorithms for distributed or multiprocessor systems. These methods are effective for data intensive applications and/or computationally intensive applications such as optimization, graph partitioning, data mining, and solving sparse linear equations. Specific research in this field focuses on grid computing, parallel algorithm design, performance analysis, and sparse matrix algorithms for large-scale scientific and engineering simulations. Software libraries developed by the group are used extensively world-wide in industry, academia and research labs.

Labs and selected projects 

• DCSG: Distributed Computing Systems Group Jon Weissman
• pARMS: parallel Algebraic Recursive Multilevel Solvers Yousef Saad
• UMN Machine Learning Seminar Series

Related centers and programs 

• Minnesota Supercomputing Institute