On estimating the useful work distribution of parallel programs under P3T: a static performance estimator

Abstract

In order to improve a parallel program's performance it is critical to evaluate how even the work contained in a program is distributed over all processors dedicated to the computation. Traditional work distribution analysis is commonly performed at the machine level. The disadvantage of this method is that it cannot identify whether the processors are performing useful or redundant (replicated) work. The paper describes a novel method of statically estimating the useful work distribution of distributed-memory parallel programs at the program level, which carefully distinguishes between useful and redundant work. The amount of work contained in a parallel program, which correlates with the number of loop iterations to be executed by each processor, is estimated by accurately modeling loop iteration spaces, array access patterns and data distributions. A cost function defines the useful work distribution of loops, procedures and the entire program. Lower and upper bounds of the described parameter are presented. The computational complexity of the cost function is independent of the program's problem size, statement execution and loop iteration counts. As a consequence, estimating the work distribution based on the described method is considerably faster than simulating or actually compiling and executing the program. Automatically estimating the useful work distribution is fully implemented as part of P3T, which is a static parameter based performance prediction tool under the Vienna Fortran Compilation System (VFCS). The Lawrence Livermore Loops are used as a test case to verify the approach.