The directory-based cache coherence protocol for the DASH multiprocessor
ISCA '90 Proceedings of the 17th annual international symposium on Computer Architecture
Proceedings of the 1st International Conference on Intelligent Systems for Molecular Biology
A methodology and an evaluation of the SGI Origin2000
SIGMETRICS '98/PERFORMANCE '98 Proceedings of the 1998 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
The hierarchical organization of molecular structure computations
RECOMB '98 Proceedings of the second annual international conference on Computational molecular biology
Parallel hierarchical molecular structure estimation
Supercomputing '96 Proceedings of the 1996 ACM/IEEE conference on Supercomputing
Topology preserving dynamic load balancing for parallel molecular simulations
SC '97 Proceedings of the 1997 ACM/IEEE conference on Supercomputing
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Molecular structure determination is an important task in biology because of the intimate relation between form and function of biological molecules. Individual sources of information about molecular structure are subject to uncertainty and are not sufficiently abundant to define the structure to high accuracy by themselves. We have examined a probabilistic algorithm, PROTEAN, which can incorporate multiple sources of uncertain data to estimate the three-dimensional structure of molecules and also predict a measure of the uncertainty in the estimated structure. We have applied this algorithm successfully to several biological structure problems. Like most structure prediction methods, this algorithm is computationally expensive for realistic biological macromolecules. In this paper, we experiment with speeding up the algorithm through the application of parallelism. We present a parallel version of the algorithm, and demonstrate good speedups on a 32-processor Stanford DASH, a cache-coherent shared-address-space multiprocessor. The results were obtained by exploiting data locality only in the per-processor coherent caches, without attempt to distribute data intelligently in the physically distributed main memory of the machine. We also obtained very good speedups on a state-of-the-art commercial multiprocessor, the Silicon Graphics Challenge. Finally, we propose an extension to the serial algorithm which enables it to handle a wider class of data, and discuss the potential for parallelization of the extended algorithm.