GTC Silicon Valley-2019: OpenACC-Based GPU Acceleration of Chemical Shift Prediction
GTC Silicon Valley-2019 ID:S9277:OpenACC-Based GPU Acceleration of Chemical Shift Prediction
Alex Bryer(University of Delaware),Sunita Chandrasekaran(University of Delaware),Juan Perilla(University of Delaware),Eric Wright(University of Delaware)
The chemical shift of a protein structure offers a lot of information about the physical properties of the protein. Being able to accurately predict this shift is essential in drug discovery and in some other areas of molecular dynamics research. But because chemical shift prediction algorithms are so computationally intensive, no application can predict chemical shift of large protein structures in a realistic amount of time. We explored this problem by taking an algorithm called PPM_One and ported it to NVIDIA V100 GPUs using the directive-based programming model, OpenACC. When testing several different protein structures of datasets ranging from 1M to 11M atoms we observed ~45X average speedup between the datasets and a maximum of a 61X speedup. We'll discuss techniques to overcome programmatic challenges and highlight the scientific advances enabled by the model OpenACC.