Accelerating Molecular Modeling with OpenACC
Example clustering analysis for a protein folding simulation.
Modern graphics processing units (GPUs) contain hundreds of arithmetic
units and can be harnessed to provide tremendous acceleration for
numerically intensive scientific applications such as molecular modeling.
The key to effective GPU computing is
the design and implementation of data-parallel algorithms
that scale to hundreds of tightly coupled processing units.
OpenACC and other directive-based parallel programming
approaches provide an important means for low-cost adaptation
of large portions of existing application codes to
heterogeneous computing platforms with GPUs and other accelerators by
leveraging the latest advances in compiler technology, parallel
runtime systems, and accelerator hardware.
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"GPU-Accelerated Molecular Dynamics Clustering Analysis with OpenACC"
- John E. Stone, Juan R. Perilla, C. Keith Cassidy, and Klaus Schulten.
- In, Robert Farber, editor, Parallel Programming with OpenACC, Morgan Kaufmann, Cambridge, MA, 2016.
- Book home pages: Amazon |
Elsevier
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OpenACC Source Code and Reference Material
Molecular Structure Alignment and Clustering Reference Material
Example clustering analysis for a protein folding simulation.
