Department of Computing Science
Parallel and High-Performance Computing Group
Umeå universitet

 

The research focuses on algorithms, tools, and applications for scalable high-performance computer systems. One important issue is the scalability of a parallel algorithm (or application), which is a measure of its capability to effectively use an increasing number of processors. Current research topics incl ude high-performance and portable linear algebra kernels, direct and iterative metho ds for linear systems and eigenvalue problems, development tools for parallel computing , and parallel algorithms in optimization (see Section \ref{OPT}). The process of developing an efficient parallel algorithm includes two major components. First, to identify and specify the overall problem as a set of tasks that can be performed concurrently. Second, to map these tasks onto different processors so that the overall communication overhead is minimized. The memory organization in current advanced computer architectures is hierarchic al. Accesses to data in the upper levels of the memory hierarchy (registers, cache and/or local memory) are much faster than those in lower levels (off-processor and shared memory). In order to approach the peak performance (measured in Mflop s) it is necessary to organize the computations such that we can maximize reuse of data in the upper levels of the memory hierarchy. By maximizing the data locality (or data reuse) we minimize the data movements within a memory hierarchy and the communication overhead between processors. There is often a tradeoff between maximizing the concurrency and the data locali ty. A technique used is to reorganize standard algorithms for linear algebra applica tions to perform matrix--matrix (level~3) operations in their inner loops. This approach is successfully used in the development of the high-performance library LAPACK. (To be revised! Add new information.)

Some ongoing projects:

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Department of Computing Science
Umeå University
The information on this page was modified 2002-12-04
Responsible for this page: Bo Kågström
 
Address: SE-901 87 Umeå, Sweden
Phone: 090-786 50 00
Fax: 090-786 61 26
E-mail: datavetenskap@cs.umu.se