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Procedures are presented that are designed to help users efficiently program irregular problems (e.g. unstructured mesh sweeps, sparse matrix codes, adaptive mesh partial dif- ferential equations solvers) on distributed memory machines. These procedures are also designed for use in compilers for distributed memory multiprocessors. The portable CHAOS pro- cedures are designed to support dynamic data distributions and to automatically generate send and receive messsage by capturing communications patterns at runtime. (Also cross-referenced as UMIACS-TR-95-34)

In adaptive irregular problems the data arrays are accessed via indirection arrays, and data access patterns change during computation. Implementing such problems on distributed memory machines requires support for dynamic data partitioning, efficient preprocessing and fast data migration. This research presents efficient runtime primitives for such problems. This new set of primitives is part of the CHAOS library. It subsumes the previous PARTI library which targeted only static irregular problems. To demonstrate the efficacy of the runtime support, two real adaptive irregular applications have been parallelized using CHAOS primitives: a molecular dynamics code (CHARMM) and a particle-in-cell code (DSMC). The paper also proposes extensions to Fortran D which can allow compilers to generate more efficient code for adaptive problems. These language extensions have been implemented in the Syracuse Fortran 90D/HPF prototype compiler. The performance of the compiler parallelized codes is compared with the hand parallelized versions. (Also cross-referenced as UMIACS-TR-94-55)

CHARMM "Chemistry at Harvard Macromolecular Mechanics" is a program that is widely used to model and simulate macromolecular systems. CHARMM has been parallelized by using the CHAOS runtime support library on distributed memory architectures. This implementation distributes both data and computations over processors. This data-parallel strategy should make it possible to simulate very large molecules on large numbers of processors. In order to minimize communication among processors and to balance computational load, a variety of partitioning approaches are employed to distribute the atoms and computations over processors. In this implementation, atoms are partitioned based on geometrical positions and computational load by using unweighted or weighted recursive coordinate bisection. The experimental results reveal that taking computational load into account is essential. The performance of two iteration partitioning algorithms, atom decomposition and force decomposition, is also compared. A new irregular force decomposition algorithm is introduced and implemented. The CHAOS library is designed to facilitate parallelization of irregular applications. This library "1" couples partitioners to the application programs, "2" remaps data and partitions work among processors, and "3" optimizes interprocessor communications. This paper presents an application of CHAOS that can be used to support efficient execution of irregular problems on distributed memory machines.