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Accelerating Protein Coordinate Conversion using GPUs

Mahsa Bayati, Jaydeep P. Bardhan, David M. King, Miriam Leeser
Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115
2014 IEEE High Performance Extreme Computing Conference (HPEC’14), 2014

@article{bayati2014accelerating,

   title={Accelerating Protein Coordinate Conversion using GPUs},

   author={Bayati, Mahsa and Bardhan, Jaydeep P and King, David M and Leeser, Miriam},

   year={2014}

}

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For modeling proteins in conformational states, two methods of representation are used: internal coordinates and Cartesian coordinates. Each of these representations contain a large amount of structural and simulation information. Different processing steps require one or the other representation. Our goal is to rapidly translate between these coordinate spaces so that a scientist can choose whichever method he or she would like independent of the coordinate representation required. An algorithm to convert Cartesian to internal coordinates is implemented by taking a protein structure file and the trajectories of protein’s atoms within a time frame. The implementation then computes bond distances, bond angles and torsion angles of the atoms. This is implemented on two types of hardware: CPU and a heterogeneous system combining CPU and GPU. The CPU sequential codes in MATLAB and C are compared with MATLAB Parallel Computing Toolbox, OpenMP, and GPU versions in CUDA-C and CUDA-MATLAB. The performance is evaluated on two different protein structure files and their trajectories. Our results show that this computation is well suited to the parallelism offered in modern Graphics Processing Units. We see many orders of magnitude improvement in speed over the original MATLAB code and have brought the computation time from over an hour down to tens of milliseconds.
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