AUTOMATIC PARALLELIZATION TECHNIQUES BASED ON COMPACT DAG EXTRACTION AND SYMBOLIC SCHEDULING

Abstract

Symbolic allocation and dynamic scheduling of tasks on a distributed memory machine for coarse-grained applications represented by parameterized task graphs (PTG) are presented in this paper. A PTG is a new computation model for symbolically representing directed acyclic task graphs (DAGs). The size of a PTG is independent of the problem size and its parameters can be instantiated at run time. Parameter independent optimization is important for exploiting non-static parallelism in scientific computing programs with varying problem sizes. Previous DAG scheduling algorithms are not able to handle such cases. We present and study a symbolic scheduling algorithm called SLC (Symbolic Linear Clustering) which derives task clusters from a PTG using affine piecewise mapping functions and then evenly assigns clusters to processors. Thus a complete automatic parallelization method is presented.