A parallel optimization method for stencil computation on the domain that is bigger than memory capacity of GPUs

2013 ◽  
Author(s):  
Guanghao Jin ◽  
Toshio Endo ◽  
Satoshi Matsuoka
Keyword(s):  
Optimization Method ◽  
Memory Capacity ◽  
Parallel Optimization ◽  
Stencil Computation

scholarly journals A Novel Objective Function DYNO for Automatic Multi-variable Calibration and Application to Assess Effects of Velocity versus Temperature Data for 3D Lake Models Calibration

2021 ◽  
Author(s):  
Wei Xia ◽  
Taimoor Akhtar ◽  
Christine A. Shoemaker
Keyword(s):  
Objective Function ◽  
Hydrodynamic Model ◽  
Model Calibration ◽  
Goodness Of Fit ◽  
Optimization Method ◽  
Parallel Optimization ◽  
Velocity Versus ◽  
Observation Data ◽  
Multiple Variables ◽  
Optimization Search

Abstract. This study introduced a novel Dynamically Normalized objective function (DYNO) for multi-variable (i.e., temperature and velocity) model calibration problems. DYNO combines the error metrics of multiple variables into a single objective function by dynamically normalizing each variable's error terms using information available during the search. DYNO is proposed to dynamically adjust the weight of the error of each variable hence balancing the calibration to each variable during optimization search. The DYNO is applied to calibrate a tropical hydrodynamic model where temperature and velocity observation data are used for model calibration simultaneously. We also investigated the efficiency of DYNO by comparing the result of using DYNO to results of calibrating to either temperature or velocity observation only. The result indicates that DYNO can balance the calibration in terms of water temperature and velocity and that calibrating to only one variable (e.g., temperature or velocity) cannot guarantee the goodness-of-fit of another variable (e.g., velocity or temperature). Our study suggested that both temperature and velocity measures should be used for hydrodynamic model calibration in real practice. Our example problems were computed with a parallel optimization method PODS but DYNO can also be easily used in serial applications.

scholarly journals Picking Path Optimization of Agaricus bisporus Picking Robot

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Xiaomei Hu ◽  
Zhaoren Pan ◽  
Shunke Lv
Keyword(s):  
Field Experiments ◽  
Search Algorithm ◽  
Optimal Path ◽  
Temperature Drop ◽  
Optimization Method ◽  
Labor Cost ◽  
Path Optimization ◽  
Parallel Optimization ◽  
Storage Location ◽  
Short Time

The design and application of the mushroom picking robot will greatly reduce the labor cost, and it has become one of the research hotspots in the world. Therefore, we independently developed an A. bisporus (a kind of mushroom) picking robot and introduced its functional principle in this paper. At the same time, in order to improve the picking efficiency of the picking robot, a picking path optimization algorithm based on TSP model is proposed. Firstly, based on the TSP model, a picking route model for A. bisporus was established to determine the storage location of each A. bisporus. Then, an improved simulated annealing (I-SA) search algorithm is proposed to find the optimal path sequence. By improving the path initialization module, path generation module, and temperature drop module, the I-SA search algorithm can optimize the picking path in a short time. Finally, in order to improve the stability and reduce the running time of the I-SA search algorithm, a parallel optimization method for global search (“rough exploration”) and local search (“precision exploration”) is proposed. Through simulation experiments, the I-SA search algorithm can search stable and excellent path solution in a relatively short time. Through field experiments on mushroom base, the efficiency of picking A. bisporus can be improved by 14% to 18%, which verifies the effectiveness of the I-SA search algorithm.

A Study of Parallel Efficiency of Modified Direct Algorithm Applied to Thermohydrodynamic Lubrication

2009 ◽  
Vol 25 (2) ◽  
pp. 143-150 ◽  
Author(s):  
N. Wang ◽  
C.-M. Tsai ◽  
K.-C. Cha
Keyword(s):  
Parallel Computing ◽  
Optimization Problems ◽  
Load Capacity ◽  
Optimization Methods ◽  
Optimization Method ◽  
Parallel Optimization ◽  
Direct Algorithm ◽  
Slider Bearing ◽  
Parallel Efficiency ◽  
Fine Grain

AbstractThis study examines the parallel computing as a means to minimize the execution time in the optimization applied to thermohydrodynamic (THD) lubrication. The objective of the optimization is to maximize the load capacity of a slider bearing with two design variables. A global optimization method, DIviding RECTangle (DIRECT) algorithm, is used. The first approach was to apply the parallel computing within the THD model in a shared-memory processing (SMP) environment to examine the parallel efficiency of fine-grain computation. Next, a distributed parallel computing in the search level was conducted by use of the standard DIRECT algorithm. Then, the algorithm is modified to provide a version suitable for effective parallel computing. In the latter coarse-grain computation the speedups obtained by the DIRECT algorithms are compared with some previous studies using other parallel optimization methods. In the fine-grain computation of the SMP machine, the communication and overhead time costs prohibit high speedup in the cases of four or more simultaneous threads. It is found that the standard DIRECT algorithm is an efficient sequential but less parallel-computing-friendly method. When the modified algorithm is used in the slider bearing optimization, a parallel efficiency of 96.3% is obtained in the 16-computing-node cluster. This study presents the modified DIRECT algorithm, an efficient parallel search method, for general engineering optimization problems.

scholarly journals A parallel optimization method for numerical solving the system of polaron equations using the partitioning algorithm

2015 ◽  
pp. 281-289
Author(s):  
А.В. Волохова ◽  
Е.В. Земляная ◽  
В.С. Рихвицкий
Keyword(s):  
Numerical Simulation ◽  
Numerical Solution ◽  
Information Technologies ◽  
Nonlinear Differential Equations ◽  
Nuclear Research ◽  
Optimization Method ◽  
Software Implementation ◽  
Parallel Optimization ◽  
Multiprocessor Systems ◽  
Partitioning Algorithm

Разработанный ранее метод численного моделирования процесса формирования поляронных состояний в конденсированных средах модифицирован с применением алгоритма разбиений, что обеспечивает существенное ускорение вычислений при расчетах в параллельном режиме на многопроцессорных системах. Программная реализация выполнена на основе технологии параллельного программирования MPI. Проведенные методические расчеты на Центральном информационно-вычислительном комплексе лаборатории информационных технологий Объединенного института ядерных исследований (Дубна) с различным количеством задействованных процессоров при выборе разных параметров вычислительной схемы подтверждают эффективность разработанного подхода для численного решения системы нелинейных дифференциальных уравнений в частных производных, описывающих динамическую модель полярона. The previously developed method for the numerical simulation of the formation of polaron states in condensed media is modified using the partitioning algorithm, which provides a significant speedup in the parallel computations on multiprocessor systems. The software implementation is based on the MPI technology. Numerical results obtained on the multiprocessor cluster installed at the Laboratory of Information Technologies (Joint Institute for Nuclear Research, Dubna) with various numbers of processors and with various computational parameters show that the proposed approach is efficient for the numerical solution of the system of nonlinear differential equations describing the polaron dynamical model.

Sign in / Sign up

Export Citation Format

Share Document