high performance fortran for highly irregular problems
We present a general data parallel formulation for highly irregular problems in High Performance Fortran (HPF). Our formulation consists of(1) a method for linearizing irregular data structures (2) a data parallel implementation (in HPF) of graph partitioning algorithms applied to the linearized data structure, (3) techniques for expressing irregular communication and nonuniform computations associated with the elements of linearized data structures.We demonstrate and evaluate our formulation on a parallel, hierarchical N--body method for the evaluation of potentials and forces of nonuniform particle distributions. Our experimental results demonstrate that efficient data parallel (HPF) implementations of highly nonuniform problems are feasible with the proper language/compiler/runtime support. Our data parallel N--body code provides a much needed "benchmark" code for evaluating and improving HPF compilers.
general data parallelism parallel method data structures implementation graph partitioning algorithm communication association demonstrate evaluation hierarchic distributed experimentation efficiency language compilation runtime support code benchmark high-performance