Concurrency and Computation: Practice & Experience

doi: 10.1002/cpe.4330080901pmid: N/A

Kindler, Thomas; Schwan, Karsten; Silva, Dilma; Trauner, Mary; Alyea, Fred

doi: 10.1002/(SICI)1096-9128(199611)8:9<639::AID-CPE233>3.0.CO;2-9pmid: N/A

The paper describes a parallel implementation of a grand challenge problem: global atmospheric modeling. The novel contributions of our work include (1) a detailed investigation of opportunities for parallelism in atmospheric global modeling based on spectral solution methods, (2) the experimental evaluation of overheads arising from load imbalances and data movement for alternative parallelization methods, and (3) the development of a parallel code that can be monitored and steered interactively based on output data visualizations and animations of program functionality or performance. Code parallelization takes advantage of the relative independence of computations at different levels in the earth's atmosphere, resulting in parallelism of up to 40 processors, each independently performing computations for different atmospheric levels and requiring few communications between different levels across model time steps. Next, additional parallelism is attained within each level by taking advantage of the natural parallelism offered by the spectral computations being performed (e.g. taking advantage of independently computable terms in equations).

Cheng, Gang; Fox, Geoffrey C.

doi: 10.1002/(SICI)1096-9128(199611)8:9<667::AID-CPE234>3.0.CO;2-9pmid: N/A

By viewing different parallel programming paradigms as essentially heterogeneous approaches in mapping ‘real‐world’ problems to parallel systems, the authors discuss methodologies in integrating multiple programming models on a massively parallel system such as Connection Machine CM5. Using a dataflow based integration model built in a visualization software AVS, the authors describe a simple, effective and modular way to couple sequential, data‐parallel and explicit message‐passing modules into an integrated parallel programming environment on a CM5. A case study in the area of numerical advection modeling is given to demonstrate the integration of data‐parallel and message‐passing modules in the proposed multi‐paradigm programming environment.

Heiss, Hans‐Ulrich; Dormanns, Marcus

doi: 10.1002/(SICI)1096-9128(199611)8:9<685::AID-CPE235>3.0.CO;2-Apmid: N/A

To execute a parallel program on a multicomputer system, the tasks of the program have to be mapped to the particular processors of the parallel machine. The aim of the mapping is twofold: (i) to achieve a balanced load on the processors (partitioning problem) and (ii) to keep communication delays low by placing communicating tasks closely together (mapping). Since both the communication structure of the program and the interconnection structure of the parallel machine can be represented as graphs, the mapping problem can be regarded as a graph embedding problem to minimize communication costs. As a new heuristic approach to this NP‐hard problem we apply Kohonen's self‐organizing maps to establish a topology‐preserving embedding. Experimental results are presented and compared to other approaches to this problem. The most attractive feature of our new method is that it can be extremely well parallelized.

Walker, D.W.; Otto, S.W.

doi: 10.1002/(SICI)1096-9128(199611)8:9<707::AID-CPE269>3.0.CO;2-Vpmid: N/A

Arrays that are distributed in a block‐cyclic fashion are important for many applications in the computational sciences since they often lead to parallel algorithms with good load balancing properties. We consider the problem of redistributing such an array to a new block size. This operation is directly expressible in High Performance Fortran (HPF) and will arise in applications written in this language. Efficient message passing algorithms are given for the redistribution operation, expressed in the standardized message passing interface, MPI. The algorithms are analyzed and performance results from the IBM SP‐1 and Intel Paragon are given and discussed. The results show that redistribution can be done in time comparable to other collective communication operations, such as broadcast and MPI_ALLTOALL.