SLC: Symbolic Scheduling for Executing Parameterized Task Graphs on Multiprocessors

Task graph scheduling has been found effective in performance prediction and optimization of parallel applications. A number of static scheduling algorithms have been proposed for task graph execution on distributed memory machines. Such an approach cannot be adapted to changes in values of program parameters and the number of processors and also it cannot handle large task graphs. In this paper, we model parallel computation using parameterized task graphs which represent coarse-grain parallelism independent of the problem size. We present a scheduling algorithm for a parameterized task graph which first derives symbolic linear clusters and then assigns task clusters to processors. The runtime system executes clusters on each processor in a multi-threaded fashion. We evaluate our method using various compute-intensive kernels that can be found in scientific applications.