Acceleration of Semiempirical QM/MM Methods through Message Passage Interface (MPI), Hybrid MPI/Open Multiprocessing, and Self-Consistent Field Accelerator Implementations

P Ojeda-May and K Nam, JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 13, 3525-3536 (2017).

DOI: 10.1021/acs.jctc.7b00322

The strategy and implementation of scalable and efficient semiempirical (SE) QM/MM methods in. CHARMM are described. The serial version of the code was first profiled to identify routines that required parallelization. Afterward, the code was parallelized and accelerated with three approaches. The first approach was the parallelization of the entire QM/MM routines, including the Fock matrix diagonalization routines, using the CHARMM message passage interface (MPI) machinery. In the second approach, two different self-consistent.field (SCF) energy convergence accelerators were implemented using density and Pock matrices as targets for their extrapolations in the SCF procedure. In the third approach, the entire QM/MM and MM energy routines were accelerated by implementing the hybrid MPI/open multiprocessing (OpenMP) model in which both the task- and loop-leveL parallelitation strategies were adopted to balance loads between different OpenMP threads. The present implementation was tested on two solvated enzyme systems (including <100 QM atoms) and an S(N)2 symmetric reaction in water. The- MPI version exceeded existing SE QM methods in CHARMM which include the SCC-DFTB and SQUANTUM methods by at least 4-fold. The use of SCF convergence accelerators further accelerated,the code by similar to 12-35% depending on the size of the QM region and the number of CPU cores used. Although the MPI version displayed good scalability, the performance was diminished for large numbers of MPI processes due to the overhead associated with MPI communications between nodes. This issue was partially overcome by the hybrid MPI/OpenMP approach which displayed a better scalability for a larger number of CPU cores (up to 64 CPUs in the tested systems).

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