Scalability analysis tools for SPMD message-passing parallel programs

Tools to study the scalability of parallel programs, as number of processors (p) executing the program and problem size (n) being solved are increased, are a critical component of performance debugging environments for parallel programs. Simulations and scalability metrics have been used to address this issue. Simulation can accurately predict the execution time of a program for a specific (n,p) pair. However, it suffers from the drawback that one needs to simulate the program for each (n,p) pair of interest. On the other hand, while scalability metrics express the program performance as functions of n and p, they have been targeted to specific applications and there are no tools to automatically obtain simple first order scalability trends for generic parallel programs. We address the issue of automatically obtaining scalability trends for a class of data-independent message passing SPMD parallel programs. We validate our approach by considering example parallel programs executed on the Intel iPSC/860 hypercube. We show that insight into the scalability of the program can be obtained, using this approach