Project Abstract:

Experimental chemical shifts (CS) from solution and solid state magic-angle-spinning nuclear magnetic resonance (NMR) spectra provide atomic level information for each amino acid within a protein or protein complex. However, structure determination of large complexes and assemblies based on NMR data alone remains challenging due to the complexity of the calculations. Here, we present a hardware accelerated strategy for the estimation of NMR chemical-shifts of large macromolecular complexes based on the previously published PPM_One software. The original code was not viable for computing large complexes, with our largest dataset taking approximately 14 hours to complete. Our results show that serial code refactoring and parallel acceleration brought down the time taken of the software running on an NVIDIA Volta 100 (V100) Graphic Processing Unit (GPU) to 46.71 seconds for our largest dataset of 11.3 million atoms. We use OpenACC, a directive-based programming model for porting the application to a heterogeneous system consisting of x86 processors and NVIDIA GPUs. Finally, we demonstrate the feasibility of our approach in systems of increasing complexity ranging from 100K to 11.3M atoms.

Awards:

~ Won one of the best research posters in the International Supercomputing Conference (ISC), 2019, Frankfurt, Germany under the HPC category.

~ Won the best poster in the VIP Mid-Altantic poster competition, Spring 2018. Prof. Chandrasekaran and team have presented this work at GTC 2019, SIAM CSE 2019 and PASC 2019.

Our Team:

Eric Wright and Mauricio Ferrato, VIP HPC students, joined this project during their junior year. Upon graduation, they joined CRPL as PhD students and continue to pursue their doctorate degrees. Robert Searles, my former PhD student, now in NVIDIA, and Alex Bryer, Prof. Perilla’s PhD student served as the senior mentors on the project.

Collaborators:

This is work in collaboration with Prof. Juan Perilla and Alex Bryer, a PhD student, from the Department of Chemistry and Biochemistry at the University of Delaware.