Performance Analysis of One Dimension FDTD Code

Performance depth psychology of sensation Dimension FDTD CodePerformance compendium of One dimension FDTD regulation utilize jibe Processing TechniqueP. GUNAPANDIAN, M.R.SUBASREE, B. MANIMEGALAIAbstractThe requirement of womb-to-tomb touch while and large memory makes FDTD method impractical for more cases. Implementation of collimate processing in FDTD method is proposed in this paper. The implementation depends mainly on computer architecture and programming libraries under several(predicate) operating organizations. In this paper a several performance tests of a atomic turning 53 dimension FDTD reckon is tested in polar platforms. The results shows that jibe processing shows a linear decrease in eon and larger data handling which makes it as a right platform for hard structures.KeywordsFDTD, check processing, computer architecture, operating system, in series(p) processing.IntroductionComputational talent has advanced in the recent few decades. One of the mo st usual methods to crop Maxwells equations on arbitrary configurations of materials and field sources is the Finite conflicts on epoch Domain (FDTD) 1. There exist a number of techniques for enhancing the performance of the pompous FDTD, in order to obtain accuracy the conformal FDTD method which simulates the curved consummate(a) electrical conductor (PEC) 2,3. Sub-gridding technique is used to increase the mesh density in the local ara in which the field varies quickly 5,6. The Multi-Resolution date-Domain (MRTD) and Pseudo-Spectrum Time-Domain (PSTD) techniques are used to reduce the dispersion of the conventional yee grid 7,8. The above techniques mentioned are used to improve the conventional FDTD technique for the purpose of reducing either memory requirements or simulation time. The replicate-processing FDTD accelerates the FDTD simulation by distributing the job to tenfold processors, so that the avail fit memory for large chores is virtually unlimited. At the said(prenominal) time, the simulation time is dramatically reduced compared to a single-processor implementation. On the computational promontory of view the parallel processing have an important emolument which makes the parallel executions strategies easier.Parallel processing in computers is ground on dividing a computer enrol into a number of segments and distribution of the task among a number of computers/processors, which are then executed in parallel. This may be achieved on hardware-level, software level, or twain. Hardware-level parallelization necessitates alternative processor designs. Software-level parallelization move be either on data level or function level, depending on the characteristics of the code 9.In this paper one dimensional FDTD code is demonstrable for parallel processing. The performance analysis of the code civilizeed is compared with both consecutive and parallel. The code is simulated in distinct platforms and the results are obtained. Th e results shows the efficiency of the parallel processing in handling larger data and reduction of time compared to ensuant processing.FDTD METHODThe FDTD method is one of the well known approaches to solve Maxwells partial differential equations, because of its high versatility. FDTD algorithm is based on temporal and three-dimensional spatial discretization and it transforms the time-dependent Maxwells curve equations into a set of finite-difference relations 10, 11.Boundary conditions are needed on the distinctness of the simulation theater of operations, among the several possible choices perfect matched layer (PML) boundary gutter be more accurate and the PML boundary is used in the developed code.One Dimension FDTDThe one dimension FDTD starts with the formulation of the Maxwells equations,Where E and H are electric and magnetic field appreciateively. The one dimension equation for Ex and Hy are given, which denotes the Electric field with respect to x direction and magn etic field with respect to y direction. The modify equations are given in (3) and (4).The FDTD update equations are used to develop the Matlab code and the code is excited with the Gaussian shiver and the results are obtained. correspond FDTDAccording to the principle of FDTD algorithm, the electromagnetic field survey at sure position can be decided by the honour of persist time step at this position and electromagnetic field value of this time step at nearby position. The electromagnetic field value has no direct relation to the value at position farther from this point. So, the whole computational space can be divided into nigh sections that can be computed in some nodes of parallel compute system. The re-sentencing of field values in the midst of nodes can be executed but at interface between sections. According to the basic concept, the computing between parallel nodes can be executed to simulate the serial computing in a single PC or workstation. This is the key po int of our parallel FDTD algorithm. Fig.1 shows the methodology of serial and the parallel approach get a line 1. straight and parallel approachPARALLEL PROCESSING TECHNIQUEThe main composition of parallel processing starts with the updating the EM field theatrical roles in distributively processor in the same instant. When the computation updates a field component on the border of the domain, some values belonging to the border of the coterminous domain are required to avoid communications during the computations each shooter domain is surrounded by the border cells of the other domain. These border values are communicated after the updating phase. Thus the parallel processing is achieved.The frontmost step of the FDTD modeling starts with the one dimensional method. A Gaussian pulse is generated in the amount of money of the problem space and the pulse propagates in the both the directions. The time step is interpreted to be 500 iteration steps and the total time required for the signal is 500fs. The time seconds are careful by the time taken by the pulse to originate from the centre and to decay at the end.The Matlab code is developed for one dimensional FDTD for serial processing and the code is updated to parallel processing. The number of cells of the computation domain in alter by keeping the number of iterations constant and the time taken by the serial and the parallel processing is noteworthy. The go steady2 shows the comparison between the serial and parallel processing for different number of cells. From the figure 2 it is noted that the parallel processing code is able to process the large number of cells within the shorter period of time, thereby proving that the parallel processing can be used to process larger amount of data in shorter duration of time.Figure 2. Serial vs. parallel processingThe parallel processing code developed has been simulated in miscellaneous newer version Intel processors which is used to take the perform ance. The number of iterations are kept constant and the cell size is varied and the code is simulated in different Intel processors include i3, i5, i7 and Pentium processors. The results shows that the updated newer version i7 was able to process the data more quickly, so that the parallel processing can be used more expeditiously in higher versions system which is easy available now a days. Figure 3 shows the Comparision with different versions of the processors. From the results it is observed that the i7 processor was able to process the code more efficiently than the others. Pentium processor which is one of the oldest among took larger time to process the code.Figure 3. Parallel processing on different versions of Intel processorsThe parallel code is simulated with different number of Matlab workers. Figure 4 shows the relation between the parallel code and the number of Matlab workers. In this code the number of iterations is kept constant and the time taken by the code to r un with different number of cells is noted. The graphical record shows that parallel code works more efficiently when the number of workers is increase to be four.Figure 4. Performance with different number of Matlab workersThe invigorate-ups increase with the problem size because of the better exploitation of CPU resources and parallel processing. Figure 5 shows the achieved speed-ups for with respect to the serial algorithm running on the CPU. Thus the parallel processing gives a better speed up for larger problems.Figure 5. Speed up comparison for serial and parallel processingFrom the figure 5 it is noted that the parallel processing gives a better speed up. With the previous results the number of iterations and the cell size is varied and the results are noted. For each cell size the iterations are changed and the values are noted. Figure 6 shows the speed up for different cell size at various iterations. From the figure it can be absorbed that larger the computation domain be tter the speed up thereby making the parallel processing very much suitable for larger computation domain.Figure 6. closed circuit vs. SpeedupCONCLUSIONFrom the results it is observed that the disadvantage of the FDTD method can be overcome by using Parallel processing FDTD method. The performance analysis of this paper thereby shows that the parallel processing can be easily achieved efficiently by using modern CPUs award today which can be used to do complex computations.REFERENCES1. Yee, K. S., numeric solution of initial boundary value problems involving Maxwells equations in isotropic media, IEEE legal proceeding on Antennas and Propagation, Vol. 14, No. 5, 302-307, May 1966.2. Time Domain Maxwell s Equations Solver Software and User s Guide, Norwood, MA, Artech House, two hundred4.3. W. Yu and R. Mittra, A Conformal FDTD Software big bucks for Modeling of Antennas and Microstrip Circuit Components, IEEE Antennas and Propagation Magazine, 42, 5, October 2000, pp. 28-39.4. W. Yu and R. Mittra, A Conformal Finite Difference Time Domain Technique for Modeling Curved Dielectric Surfaces,IEEE Microwave and command Wave Letters, January 2001, pp. 25-27.5. W. Yu and R. 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