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GPU accelerated implementation of NCI calculations using promolecular density
Author(s) -
Rubez Gaëtan,
Etancelin JeanMatthieu,
Vigouroux Xavier,
Krajecki Michael,
Boisson JeanCharles,
Hé Eric
Publication year - 2017
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.24786
Subject(s) - cuda , computer science , compiler , parallel computing , graphics processing unit , graphics , computational science , central processing unit , leverage (statistics) , general purpose computing on graphics processing units , code (set theory) , energy consumption , set (abstract data type) , operating system , programming language , machine learning , biology , ecology
The NCI approach is a modern tool to reveal chemical noncovalent interactions. It is particularly attractive to describe ligand–protein binding. A custom implementation for NCI using promolecular density is presented. It is designed to leverage the computational power of NVIDIA graphics processing unit (GPU) accelerators through the CUDA programming model. The code performances of three versions are examined on a test set of 144 systems. NCI calculations are particularly well suited to the GPU architecture, which reduces drastically the computational time. On a single compute node, the dual‐GPU version leads to a 39‐fold improvement for the biggest instance compared to the optimal OpenMP parallel run (C code, icc compiler) with 16 CPU cores. Energy consumption measurements carried out on both CPU and GPU NCI tests show that the GPU approach provides substantial energy savings. © 2017 Wiley Periodicals, Inc.