Abstract
The optimization techniques for hierarchical O(N) N-body algorithms described here focus on managing the data distribution and the data references, both between the memories of different nodes and within the memory hierarchy of each node. We show how the techniques can be expressed in data-parallel languages, such as High Performance Fortran (HPF) and Connection Machine Fortran (CMF). The effectiveness of our techniques is demonstrated on an implementation of Anderson's hierarchical O(N) N-body method for the Connection Machine system CM-5/5E. Of the total execution time, communication accounts for about 10–20% of the total time, with the average efficiency for arithmetic operations being about 40% and the total efficiency (including communication) being about 35%. For the CM-5E, a performance in excess of 60 Mflop/s per node (peak 160 Mflop/s per node) has been measured.
Highlights
Achieving high efficiency in hierarchical methods on massively parallel architectures is an important problem
Hierarchical methods are the only feasible methods for large-scale computational problems involving many-body interactions, such as astrophysical simulations and molecular dynamics simulations including long-range forces. This artiele examines techniques for achieving high efficiency in implementing nonadaptive O(N) N-body algorithms on massively parallel processors (MPPs)
We have demonstrated that exploiting parallelism within each level of the hierarchy can yield high efficiency
Summary
Achieving high efficiency in hierarchical methods on massively parallel architectures is an important problem. Hierarchical methods are the only feasible methods for large-scale computational problems involving many-body interactions, such as astrophysical simulations and molecular dynamics simulations including long-range forces. This artiele examines techniques for achieving high efficiency in implementing nonadaptive O(N) N-body algorithms on massively parallel processors (MPPs). It provides Connection ~achine Fortran (CMF) [1] code fragments that illustrate. *\li.ork conducted when author was with Thinkinl!
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