Partitioning the global space for distributed memory systems

Partitioning the iteration space can significantly affect the execution time of a loop. The authors propose an improvement over previous partitioning methods for single loops with uniform data dependencies. For distributed memory systems, partitioning each loop separately does not guarantee an efficient execution of the code because of across loop data dependence. As a result, a global iteration space is formed so that all loops in a program are considered when partitioning the global space. In addition, a new and general form of data dependence called hyperplane dependence is introduced and used in the partitioning. It is a dependence whose source and destination are subspaces (of any dimension) of the global iteration space.