Using Machine Descriptors to Select Parallelization Models and Strategies on Hierarchical Systems 

Mark Yankelevsky, Walden Ko, Dimitrios S. Nikolopoulos, and 
Constantine Polychronopoulos, CSRD/University of Illinois

Abstract:

Clusters present the programmer with a complex hierarchy of hardware 
components, exploiting different levels of parallelism. The optimal 
parallelization strategy depends on several parameters, such as the number of 
nodes, processors per node, memory and communication bandwidth, and the 
overhead of orchestrating parallelism. A compiler using a detailed machine 
descriptor and static performance analysis can automate the selection of the 
best strategy. Experiments with the NAS benchmarks (parallelized using a 
combination of MPI and OpenMP) revealed performance patterns that drive the 
selection. Results and derived algorithms are presented in the poster and 
incorporated into the machine description of the PROMIS 
(HTTP://promis.csrd.uiuc.edu) compiler.