The large scale parallelization of a conformational 3D Protein structure prediction application

  • Authors:

  • Philip LoCascio;Kaizhi Yue;Peter Cummings;Ken Dill

  • Affiliations:

  • Center for Computational Sciences, Oak Ridge, TN;University of California-San Francisco, San Francisco, CA;University of Tennessee, Knoxville, TN and Oak Ridge National Laboratory, Oak Ridge, TN;University of California-San Francisco, San Francisco, CA

  • Venue:
  • SC '98 Proceedings of the 1998 ACM/IEEE conference on Supercomputing
  • Year:
  • 1998

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Abstract

We present here the design strategy and performance analysis of a large scale scientific application, for the prediction of 3D Protein structures. The unique challenges which will be investigated are the primary objectives of a reduction in wall clock run time through the parallelization process, and the production of an application capable of running and scaling to a massively parallel configuration (currently 1024 nodes of the Intel Paragon) reliably for many non-contiguous days of supercomputer time. Enough flexibility to be reconfigured for a number of different parallel architectures including the CRAY T3E and IBM SP2 was included through the use of MPI as the parallel software layer.The application, GEOCORE, predicts small ensembles of native-like peptide conformations from amino acid sequences. GEOCORE uses a very simple energy function and an extensive conformational search process. The serial program has been tested on around 20 small peptides and is shown to be capable of discriminating native from non-native structures.