Study And Implement On Distributed Grand Canonical Monte Carlo Simulation Algorithm

In this paper, our works is of development to the Monte Carlo program, which can simulate the density distribution of Lennard-Jones fluids, confined in square nanoscale channels with Lennard-Jones walls, and decrease the time of convergence. The Monte Carlo simulation is ubiquitous in the fields of mathematics, physics and engineering and technologies. Monte Carlo algorithm has the merits of simple, flexible and etc, but this algorithm is restricted by speed of convergence. In order to improve bit accuracy, the one hundred-time simulation iteration will be increased. We studied and implement a distributed algorithm to quicken Grand Canonical Monte Carlo (GCMC) simulation convergence and improve the efficiency of simulation. The whole distributed simulation system represents a single user interface as same as the serial Monte Carlo simulation program, and the other nodes in the LAN are of transparence for system user. The implementation of algorithm employed the technologies of RMI, Applet, Servlet and JSP technologies. This algorithm harmonizes the autonomic computers in the LAN to run simulation program in parallel and readjust the loading balance dynamically to wipe off the bottle-neck of speed. By the detecting the status of nodes during the simulation, the program can find the halted nodes and move it's task to the running nodes. In this paper, we sum up the results from the different clients with the weight to increase the influence of client running more iteration. In the chap 4, we measured the acceleration with different number of client, and the result is satisfied. Density distribution of Lennard-Jones fluids confined in square nanoscale channels with Lennard-Jones walls was simulated with our program, and the results were compared with results of DFT and no parallel Monte Carlo simulation, that prove the correctness of algorithm. In order to simulate the density distribution of Lennard-Jones fluids confined different Lennard-Jones walls, we employed dependence injection (DI) of Spring to postpone the dependence relation of system on wall to runtime, so the simulation system can exchange the wall dynamically, and support variable wall potential to improve the reuse of this program. On the basise of researching on the distributed system architecture, we combine the technologies of J2EE specifications to design and code the distributed GCMC system which holds on the high accuracy. The algorithm overcomes the shortcoming of old GCMC simulation algorithm, and shows a new way to improve the convergence speed of simulation.