TY - JOUR

T1 - Scalability of the parallel strongin algorithm in the problem of optimizing a molecular-dynamic force field

AU - Shefov, K. S.

AU - Stepanova, M. M.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Strongin's multifactorial global search algorithm (MGSA) allows one to find an absolute minimum of a function of multiple variables on a mesh. In this contribution a parallel program is presented that implements the algorithm above applied to ReaxFF MD force field parameters search. In case of ReaxFF optimization, computation time of an objective function value significantly exceeds time of data exchange between parallel processes. One is able to speed up computation by obtaining not only one but several function values in various points simultaneously. Our software implements two levels of parallelism To deal with function of multiple variables, one uses a scan for mapping a multidimensional domain of definition of a function into a one-dimensional segment. To decrease the effect of losing information of multi-dimensional points proximity, N scans are used. Function values in N different mesh points are computed in parallel. This is the first level of parallelism To define a mesh point of a next iteration, MGSA finds a subinterval with the most probable location of the minimum and computes an objective function value in a certain point of this subinterval. Function values are also calculated in parallel in (M - 1) subintervals with less probability. This is the second level of parallelism Thus the two levels allow one to compute M · N function values in parallel each iteration. In this contribution we research scalability of our MGSA implementation, namely, the dependence of the number of algorithm iterations and time it needs to converge on the number of CPU cores used, separately for each level of parallelism.

AB - Strongin's multifactorial global search algorithm (MGSA) allows one to find an absolute minimum of a function of multiple variables on a mesh. In this contribution a parallel program is presented that implements the algorithm above applied to ReaxFF MD force field parameters search. In case of ReaxFF optimization, computation time of an objective function value significantly exceeds time of data exchange between parallel processes. One is able to speed up computation by obtaining not only one but several function values in various points simultaneously. Our software implements two levels of parallelism To deal with function of multiple variables, one uses a scan for mapping a multidimensional domain of definition of a function into a one-dimensional segment. To decrease the effect of losing information of multi-dimensional points proximity, N scans are used. Function values in N different mesh points are computed in parallel. This is the first level of parallelism To define a mesh point of a next iteration, MGSA finds a subinterval with the most probable location of the minimum and computes an objective function value in a certain point of this subinterval. Function values are also calculated in parallel in (M - 1) subintervals with less probability. This is the second level of parallelism Thus the two levels allow one to compute M · N function values in parallel each iteration. In this contribution we research scalability of our MGSA implementation, namely, the dependence of the number of algorithm iterations and time it needs to converge on the number of CPU cores used, separately for each level of parallelism.

KW - Absolute extremum search

KW - Chemically reactive systems

KW - Molecular dynamics

KW - Numerical simulation

KW - Parallel algorithm

KW - Parameter optimization

KW - Reactive force field

KW - Scalability

UR - http://www.scopus.com/inward/record.url?scp=85060110170&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85060110170

VL - 2267

SP - 605

EP - 610

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

SN - 1613-0073

T2 - 8th International Conference "Distributed Computing and Grid-Technologies in Science and Education", GRID 2018

Y2 - 10 September 2018 through 14 September 2018

ER -