Development of high performance casting analysis software by coupled parallel computation. *Sang Hyun CHO, Jeong Kil CHOI

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1 August 2007 Development of high performance casting analysis software by coupled parallel computation *Sang Hyun CHO, Jeong Kil CHOI (Center For e-design, Korea Institute of Industrial Technology, , Dongchun-Dong, Yeonsu-Gu, Incheon, , Korea) Abstract: Up to now, so much casting analysis software has been continuing to develop the new access way to real casting processes. Those include the melt flow analysis, heat transfer analysis for solidification calculation, mechanical property predictions and microstructure predictions. These trials were successful to obtain the ideal results comparing with real situations, so that CAE technologies became inevitable to design or develop new casting processes. But for manufacturing fields, CAE technologies are not so frequently being used because of their difficulties in using the software or insufficient computing performances. To introduce CAE technologies to manufacturing field, the high performance analysis is essential to shorten the gap between product designing time and prototyping time. The software code optimization can be helpful, but it is not enough, because the codes developed by software experts are already optimized enough. As an alternative proposal for high performance computations, the parallel computation technologies are eagerly being applied to CAE technologies to make the analysis time shorter. In this research, SMP (Shared Memory Processing) and MPI (Message Passing Interface) (1) methods for parallelization were applied to commercial software "Z-Cast" to calculate the casting processes. In the code parallelizing processes, the network stabilization, core optimization were also carried out under Microsoft Windows platform and their performances and results were compared with those of normal linear analysis codes. Keywords: parallel computation; message passing interface; casting analysis; SMP; performance improvement CLC number: TP391.9/TG244 Document Code: A Article ID: (2007) These days the computational techniques are matured enough with hardware improvements, and their benefits are directly given to simulation developers to enable the huge scientific calculations. Especially, trials for large scale analysis, what we could not even imagine about 10 years ago, become possible to carry out. The analysis for casting processes gets into the same situation with other simulation fields of different manufacturing processes, so as, the developments of coupled modeling methods and computational techniques are eagerly being carried out using PC grade computers, although it was not able to do the same job by mid or high range performance computers. Endeavors to accelerate the computational performance had been carried out through code optimization techniques as software technical aspect and the simplification of physical. Accompanying with those applications, unceasing improvements of hardware performance had been continued to make the computation time shorter with high accuracies *Sang Hyun CHO Male, born in 1970, PhD. Senior researcher. Research interests: simulation and development for microstructure prediction, solidification analysis, parallel computation. chosh@kitech.re.kr Received: ; Accepted: comparing with experimental results. Among so many performance improvement methodologies, the commercial software developers for scientific analysis are paying enough attention to the parallel computation because of its high capacities to improve the computing performances without or with only a few additional works, and these trends in developing the casting process simulation softwares exactly correspond with requests from manufacturing field users today. In case to develop new casting design, the melt flow and heat transfer simulations had been strong tools to analyze and predict the physical phenomenon, and they passed long-term verifications by field tests for real products. Mainly these two simulation tools for casting processes have become common sense for flawless casting design, so that their applications on manufacturing fields have become inevitable in whole developing work flow to develop new products or casting items. Owing to well grown simulation techniques, developing or designing processes of new casting products could save time and cost with high efficiencies. In different simulation fields for casting processes, microstructure prediction, structural analysis considering the stress and deformation phenomenon and so many valuable works become actual trials and they are reporting successful stories by 215

2 CHINA FOUNDRY good results coinciding with experimental works. With increasing computational jobs, the loads burdened on computers increase, so that computational environments become strict. The simulation software users request for faster software and hardware ceaselessly to satisfy with needs for more realistic and complex simulation results. The parallel computation can relax or free us from resource shortages such as memory and CPU performance, so that large scale analysis or micro-level modeling can be carried out successfully. In this research, as first step to develop parallelized simulation codes for casting processes, a commercial heat transfer analysis Vol.4 No.3 solver was developed using MPI (Message Passing Interface) and SMP (Shared Memory Processing) [2], and these programmatic techniques were coupled to improve the computing performance corresponding to computational environments. The efficiency of coupled parallelization method was compared with that of normal codes. 1 Methodologies The parallelization methods are divided into two main technologies. Those are (1) SMP, and (2) MPI and their schematic concepts are shown in Fig. 1. (a) SMP (b) MPI Fig. 1 Schematic diagrams for parallelization methods In case of using the SMP method, the solver is executable only at unique machine and memories and resources on that unique machine are shared in whole solving thread. That means, the extra resources including memories for every CPU are not necessary, because the multi-threads are executed on same machine, so that CPUs on same machine can access and handle the data stored on same storage spaces. These characteristics are the representative advantages of SMP method, and they give the freedom to solver developers because extra codes to handle data for every CPU privately are not necessary. As a result, less additional programming work is necessary compared with the other parallelization method, and it is directly connected to efficiencies of development. Although SMP methods have many advantages in developing the solver, it has critical disadvantage i. e. low expansion abilities of CPUs, and only quad CPU systems are available at PC grade. In spite of this disadvantage, SMP is a powerful parallelization method for solver developers. MPI method is another parallelization tool used at super computer. Basically this method offers parallelization on networks, that is, the parallelized program is executed at multinode computers connected by networks. Various types of network can be used to connect the computers, and representative methods are TCP/IP and Infini Band for broadband connections. In case to develop the solvers using MPI method, a developer should be careful enough to make efficient codes, because MPI does not share any resources even storages, so that the following points should be considered. (1) Data transferring optimization to reduce the network loads (2) Load balancing to use 100% computing power from unbalanced computer performances (3) Automatic MPI sizing functions depending on the computer resources Even though these topics are considered in developing the MPI parallelized solvers, it is difficult to make normal linear code to parallelized one because of its complexities. In this research, as first, solidification solver was parallelized by SMP method using Open MP API. Especially, the loops calculating heat balances were parallelized, and to maximize the parallelization efficiency, the codes in the loops were optimized to prevent the cash data crashes during the looping runs. Then, MPI codes were newly developed to predict the solidification process. Of course MPI codes include the SMP codes in them to share the resources on local machine. The concept of MPI and SMP coupled parallelization is shown in Fig Development Process The SMP and MPI type solvers were developed under Windows XP environments and they can be executed under every Windows platform. Comparing to UNIX environment to develop MPI solvers, Windows OS has so many problems because excepting for Windows native programs, Windows does not supply any network tools between different processes. It means the solver execution process can not be accessed by its control process, so that the program execution control becomes difficult when the sudden internal error occurs. To 216

3 August 2007 Fig. 2 SMP and MPI coupled parallelization execution flow control solver executions, the service code was developed. The service code helps to exchange signals or control codes between MPI solver and control program, and its control system is shown in Fig. 3. To reduce the data transferring size to exchange the boundary conditions between computers on networks, automatic mesh optimization method was developed and applied to Z-Cast. It decides the data rearrangement directions automatically, and then the performances of participating computers are automatically measured to decide the size of work to be assigned to each computer. This procedure resizes the calculation size of each computer according to their performances as shown in Fig. 4. Fig. 3 Parallelized solver control system for Z-Cast (a) Wrong balanced setting for 2 nodes MPI analysis (b) Automatic load balancing for solidification analysis Fig. 4 Schematics of automatic load balancing function 217

4 CHINA FOUNDRY The automatic load balancing also detects the CPU performances, so that it can decide the CPU numbers to use at local machine for SMP executions. According to prescribed criterion, the solver can enable or disable the SMP CPU usage. To exchange and move data on memory to storage peripherals, MPI 2.0 was used. MPI 2.0 has the functions to access to remote memory separated by networks, and it can help data exchanging works. And to reduce the memory usage at local machine for increasing analysis size, multi functional accept layer technology was developed, so that with only one functional accept layer array, client computers can send their full-size data array to server, and server can maintain the data structure. 3 Applications and Comparisons To verify the developed solver, the following systems were used, Vol.4 No.3 and in order to verify every developed function, each computer has different performances and resource sizes. The computers are being connected by Gigabit TCP/IP. Using these systems, the solidification process of cylinder block for V6 engine was calculated. The used meshes and calculated results are shown in Fig. 5. The STL data for V6 engine was divided into about 60 million FDM meshes and the meshes were rearranged automatically to reduce the data communications between computers, then the performance test section was executed for every machine. It decides the analysis size differently for each machine, and then the decided work burdens were transferred to each client. During the analysis, network load recorded the maximum load peak lower then 1% usage of 1 Gbps and it fluctuated repeatedly with normal regularity. This shows that the network balancing and optimization methods developed in this research are effective and they work well. (a) Mesh generation (b) 10% solidified (c) 50% solidified (d) 90% solidified Fig. 5 Solidification analysis for V6 Engine block To compare the analysis performance, the following model was applied as shown in Fig. 6 and the performance comparison results are shown in Table 1 and Table 2. According to Table 2, the result by 2 CPU (SMP) was 1.51 times faster than normal codes, and 4 CPU calculation by MPI and SMP was 2.8 times faster than normal codes. These results show that the parallelization is an effective way to increase the analysis performances, and the results in Table 2 can be improved further through code optimizations. And by using the MPI based parallelization method, the analysis scale can increase with increase of participating client numbers. (a) Casting design CAD Fig. 6 Performance testing model (b) Meshes 4 Conclusions With increase of analysis scale, high performance analysis method becomes necessary to carry out the accurate predictions. So that in the research, the parallelization methods were applied to commercial solidification analysis software Z-Cast. The SMP and MPI methods are two main streams in the parallel computing field, and using them together, a coupled parallelized solidification analysis code was developed. Especially to maximize the parallelization efficiencies, automatic load 218

5 August 2007 Table 1 Used Server-client systems for SMP-MPI solidification analsis Main Server Node 1 Node 2 Node 3 CPU 3.0 GHz 3.0 GHz 3.2 GHz 3.2 GHz Memory 4 GByte 3 GByte 2 GByte 2 GByte Normal Liner Solver Calculation time, s Table 2 Performance comparison results Normal Liner Solver SMP calculation (2 CPU) SMP+MPI (4 CPU) (1.51 times faster) (2.8 times faster) balancing, automatic resource distributing, and auto CPU selecting methods were developed. And to execute developed codes under Windows platforms, new method to control the execution processes was developed and verified through applying them to commercial software Z-Cast. The developed SMP code, or SMP and MPI coupled codes showed high efficiencies in analyzing the solidification processes, and they will be extended to commercial melt flow analysis and structural analysis. References [1] [2] William Gropp, Ewing Lusk, Rajeev Thakur. Advanced Features of the Message-Passing Interface, Using MPI-2. The MIT Press, Achal Prabhakar, Vladimir Getov. Performance Evaluation of Hybrid Parallel Programming Paradigms. Workshop on Performance Analysis and Distributed Computing,