Exploring the Feasibility of 2D Matrix Partitioning

Sandia National Laboratories Computer Science, 2010-11

Liaison(s): Michael Wolf ’98, Erik Boman, Karen Devine, Bill Spotz
Advisor(s): Christopher Stone
Students(s): Audrey Lawrence (PM-S), Michael Leece (PM-F), Joe DeBlasio, Katie Ewing

Sandia National Labs has developed the Trilinos software framework for large-scale scientific and engineering problems. Large, sparse, matrix-vector multiplications arise frequently in their problems, and distributing the matrix and vector among many processors can produce significant speedup. We extended the Trilinos support for distributing large matrices, including additional partitioning algorithms and code to visualize and evaluate these partitions, and performed an empirical study of the results.