Concurrency and Computation: Practice & Experience

doi: 10.1002/cpe.4330050801pmid: N/A

De Leone, R.; Roth, M. A. Tork

doi: 10.1002/cpe.4330050802pmid: N/A

Serial and parallel successive overrelaxation (SOR) solutions of specially structured large‐scale quadratic programs with simple bounds are discussed. By taking advantage of the sparsity structure of the problem, the SOR algorithm was successfully implemented on two massively parallel Single‐Instruction‐Multiple‐Data machines: a Connection Machine CM‐2 and a MasPar MP‐1. Computational results for the well known obstacle problems show the effectiveness of the algorithm. Problems with millions of variables have been solved in a few minutes on these massively parallel machines, and speed‐ups of 90% or more were achieved.

Marinescu, Dan C.; Rice, John R.; Cornea‐Hasegan, Marius A.; Lynch, Robert E.; Rossmann, Michael G.

doi: 10.1002/cpe.4330050803pmid: N/A

The paper discusses algorithms and programs for electron density averaging using a distributed memory MIMD system. Electron density averaging is a computationally intensive step needed for phase refinement and extension in the computation of the 3‐D structure of macromolecules like proteins and viruses. The determination of a single structure may require thousands of hours of CPU time for traditional supercomputers. The approach discussed in this paper leads to a reduction by one to two orders of magnitude of the computing time. The program runs on an Intel iPSC/860 and on the Touchstone Delta system and uses a user controlled shared virtual memory and a dynamic load‐balancing mechanism.

Stein, Josef; Fox, Geoffrey C.

doi: 10.1002/cpe.4330050804pmid: N/A

We have used six static parallelization tools on four Fortran‐77 programs used in physics simulations. We indicate areas where current tools have difficulties in recognizing parallelism, and illustrate these issues with simple examples. We suggest that a dynamic dependency analysis tool is needed to aid the user in the parallelization of dusty decks.

Zhang, Hiaodong; Wang, Beichang

doi: 10.1002/cpe.4330050805pmid: N/A

Multistage Interconnection Networks (MIN) have been widely used for building large‐scale shared‐memory multiprocessor systems. Complex interactions between many processors and memory modules through the MIN, (such as interprocessor communication, process scheduling and synchronization and remote‐memory access) result in a significantly large space of possible performance behavior and potential performance bottlenecks. To provide insight into dynamic system performance, we have developed an integrated data collection, analysis, and visualization environment for a MIN‐based multiprocessor system, called MIN‐Graph. The MIN‐Graph is a graphical instrumentation monitor to aid users in investigating performance problems and in determining an effective way to exploit the high performance capabilities of interconnection network multiprocessor systems. Interconnection network contention is a major bottleneck of parallel computing on MIN‐based multiprocessors. This paper focuses on evaluating the contention behavior through performance monitoring and visualization. Four sets of system and scientific application programs with different programming and scheduling models and different memory access patterns are monitored and tested to observe the various network contention behaviors. The MIN‐Graph is implemented on the BBN GP1000 and the BBN TC2000.