Parallelizing
Molecular Dynamics Programs for Distributed Memory Machines: An Application
of the CHAOS Runtime Support LibraryAlso published as: University of Maryland: Department of Computer Science and UMIACS Technical Reports CS-TR-3374, UMIACS-TR-94-125
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 decompositions and force decomposition, is also compared. A new irregular force decompositional 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 and application of CHAOS that can be used to support efficient execution of irregular problems on distributed memory machines.
Parallelizing
Molecular Dynamics Codes using PARTI Software PrimitivesPublished in:
This paper is concerned with the implementation of the molecular dynamics code, CHARMM, on massively parallel distributed-memory computer architectures using a data-parallel approach. The implementation is carried out by creating a set of software tools, which provide an interface between the parallelization issues and the sequential code. Large practical MD problems is solved on the Intel iPSC/860 hypercube. The overall solution efficiency is compared with that obtained when implementation is done using data-replication.
Run-time
and Compile-time Support for Adaptive Irregular Problems
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.
Communication
Optimizations for Irregular Scientific Computations on Distributed Memory
Architectures
Also published as: University of Maryland: Department of Computer Science and UMIACS Technical Reports CS-TR-3163, UMIACS-TR-93-109
This paper describes a number of optimizations that can be used to support the efficient execution of irregular problems on distributed memory parallel machines. These primitives (1) Coordinate interprocessor data movement, (2) manage the storage of, and access to, copies of off-processor data, (3) minimize interprocessor communication requirements and (4) support a shared name space. We present a detailed performance and scalability analysis of the communication primitives. This performance and scalability analysis is carried out using a workload generator, kernels from real applications and a large unstructured adaptive application (the molecular dynamics code CHARMM).
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