Efficient Data Input/Output (I/O) for Finite Difference Time Domain (FDTD). Computation on Graphics Processing Unit (GPU)

Master's Thesis , 2014 , 101 Pages , Grade: First

Computer Science - Applied

Due to recent advancement in technology, one of the popular ways of achieving performance with respect to execution time of programs is by utilizing massive parallelism power of GPU-based accelerator computing along with CPU computing. In GPU- based accelerator computing, the data intensive or computationally intensive part is computed on the GPU whereas the simple yet complex instructions are computed on the CPU in order to achieve massive speedup in execution time of the computer program executed on the computer system.

In physics, especially in electromagnetism, Finite-Difference Time-Domain (FDTD) is a popular numerical analysis method, which is used to solve the set of Maxwells partial differential equations to unify and relate electric field with magnetic field. Since FDTD method is computationally intensive and has high level of parallelism in the computational implementation, for this reason for past few years researchers are trying to compute the computationally intensive part of FDTD methods on the GPU instead of CPU. Although computing parallelized parts of FDTD algorithms on the GPU achieve very good performance, but fail to gain very good speedup in execution time because of the very high latency between the CPU and GPU. Calculation results at each FDTD time-step is supposed to be produced and saved on the hard disk of the system. This can be called as data output of the FDTD methods, and the overlapping of data output and computation of the field values at next time step cannot be performed simultaneously. Because of this and latency gap between the CPU and GPU, there is a bottleneck in the performance of the data output of the GPU. This problem can be regarded as the inefficient performance of data input/output (I/O) of FDTD methods on GPU.

Hence, this project focuses on this aforementioned problem and addresses to find solutions to improve the efficiency of the data I/O of FDTD computation on GPGPU (General Purpose Graphics Processing Unit).

Contents

Abstract

Declaration

Copyright

Acknowledgements

1 Introduction
1.1 Computation in Electromagnetism
1.1.1 Maxwell’s Equations
1.1.2 Finite-Difference Time-Domain (FDTD)
1.2 Computational Parallelization Techniques & GPGPU
1.2.1 Parallel Computer Architecture
1.2.2 Parallel Algorithms & Programs
1.2.3 Emerging Parallelization Techniques: GPGPU
1.3 The Problem and The Objective
1.4 Thesis Overview
1.5 Original Contribution

2 Electromagnetism & Finite-Difference Time-Domain - Overview
2.1 Maxwell’s Equations
2.2 Finite-Difference Time-Domain (FDTD)
2.2.1 Frequency Dependent Material Parameters & Frequency De­pendent FDTD
2.2.2 Boundary Condition
2.3 Summary of Maxwell’s Equations and FDTD Method
2.4 Computer Implementation of FDTD Method
2.4.1 Basics of FORTRAN 90 Programming
2.4.2 Implementation of FDTD Method
2.5 Advantages and Limitations of FDTD Computation
2.6 Concluding Remarks

3 Computation of FDTD on GPGPU using CUDA Programming
3.1 GPGPU - The Parallelization Monster and Computation Techniques .
3.2 CUDA and CUDA Fortran
3.3 CUDA Implementation of FDTD Method for GPGPU Computation .
3.4 Computation on Nvidia’s General Purpose GPU
3.4.1 GPU Hardware and support for FDTD Computation
3.4.2 Memory Coalescing
3.5 Execution of FDTD Method on GPU Hardware
3.6 Concluding Remarks

4 The Solution to The Problem
4.1 The Problem - Revisited
4.2 The Solution
4.3 Programmatic Implementation of the Solution
4.3.1 Implementation
4.3.2 Invoking Buffer Kernel
4.4 Possible Limitations and their Solutions
4.5 Concluding Remarks

5 Evaluation and Validation of The Solution
5.1 Testing of the Implemented Solution
5.1.1 Input Parameters for FDTD Computation
5.1.2 Hardware Environment
5.1.3 Test Results
5.2 Critical Analysis & Evaluation of Test Results
5.2.1 Speed-Up Analysis
5.2.2 Evaluation and Comments

6 Conclusion and Future Scope
6.1 Future Scope
6.2 Conclusion

Bibliography

A Survey Questions posted
A.1 Survey Questions posted via email and Researchgate OSN platform .

• College: University of Manchester (School of Computer Science)
• Course: Advanced Computer Science: Computer Systems Engineering
• Grade: First
• Author: Somdip Dey (Author)
• Publication Year: 2014
• Pages: 101
• Catalog Number: V462250
• ISBN (eBook): 9783668939547
• ISBN (Book): 9783668939554
• Language: English
• Tags: HPC, GPU, FDTD, GPGPU, CPU, electro magnetics, fortran, CUDA, Finite difference methods, ime domain analysis, parallel programming, parallel computing, OpenACC, multi-core computing, paralle architectures, high performance computing, data I/O, buffer