Parallel Computing in Numerical Simulation of Laser Deposition

1. Parallel Computing in Numerical Simulation of Laser Deposition Students: Xueyang Chen, Zhiqiang Fan, Todd E.Sparks, Department of Manufacturing Engineering Faculty Advisor: Dr. Frank Liou, Department of Manufacturing Engineering Approach Objective Results of simulation Conclusions • Using MPI in parallel computing seems to become more efficient with a higher number of processes. • Initial partitioning can be very crucial in parallel computing with MPI. • With variable partitioning in each step considering the available processes, some processes may not be efficiently used. In fact for the worst case with a higher number of processes for a small data set, some processes may leave out without any data to sort sequentially which shows inefficient use of processes. The objective of this proposed project is to research and develop an effective prediction tool for additive manufacturing processes for advanced materials and develop experimental methods to provide fundamental properties and establish validation data. Key Concepts of MPI • Used to create parallel programs based on message passing • Normally the same program is running on several different processors • Processors communicate using message passing • MPI is used to create parallel programs based on message passing • Usually the same program is run on multiple processors • The 6 basic calls in MPI are: • MPI_INIT(…); • MPI_COMM_SIZE(…); • MPI_COMM_RANK(…); • MPI_SEND(…); • MPI_RECV(…); • Call MPI_FINALIZE(…); Future work • The first step is to establish macroscopic models including the thermal/fluid dynamics models and the residual stress model. • Then we should also establish microscopic models,, such as the models for solidification microstructure and the solid-state phase transformations. We need modeling as-received grain structure, melting and epitaxial growth. • The last thing is to do data collection and model validation. Figure 2. Macroscopic simulation results Figure 1. Laser deposition Introduction Write a parallel program: #include <stdio.h> #include "mpi.h" main( int argc, char *argv[] ) { int myid,numprocs; MPI_Init( &argc, &argv ); MPI_Comm_rank( MPI_COMM_WORLD, &myid ); MPI_Comm_size( MPI_COMM_WORLD, &numprocs ); printf(“I am %d of %d\n", myid, numprocs ); MPI_Finalize(); } Parallel computing is accomplished by splitting up a large computational problem into smaller tasks that may be performed simultaneously by multiple processors. • MPI stands for “Message Passing Interface”. • Library standard defined by a committee of vendors, implementers, and parallel programmers • Used to create parallel programs based on message • 100%portable: one standard, many implementations • Available on almost all parallel machines in C and Fortran • Over 100 advanced routines but 6 basic Acknowledgments • This research was partially supported by the National Aeronautics and Space Administration Grant Number NNX11AI73A, the grant from the U.S. Air Force Research Laboratory, and Missouri S&T’s Intelligent Systems Center and Manufacturing Engineering program. Their support is greatly appreciated. Figure 3. Evolution of solidification microstructure