PPKE ITK PhD and MPhil Thesis Classes

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2 1. INTRODUCTION The motivation of the dissertation is to develop a methodology for solving partial differential equations, especially for liquid and gas flow simulations, which helps to map these problems optimally into inhomogenous and reconfigurable architectures. To reach this goal two hardware platforms as experimental framework were built up, namely the IBM Cell Broadband Engine Architecture and the Xilinx Field Programmable Gate Array (FPGA) as reconfigurable architecture. After the creation of the framework, which models fluid flow simulations, it is mapped to these two architecture which follows different approach (see Chapter 2). To fully utilize the capabilities of the two architecture several optimization procedure had to performed. Not only the structure of the architectures are taken into consideration, but the computational precision too (introduced in Chapter 3). It is important to examine the precision of the arithmetic unit on FPGA, because significant speedup or lesser area requirement and power dissipation can be achieved. With the investigation of the computational precision, the decision can be taken, wether the problem fits onto the selected FPGA or not. It relies mainly on the number of operations in the arithmetic unit. To get a complete, standalone machine, the processing element on FPGA should be extended with a control unit (it is going to e introduced in Chapter 4). The IBM Cell processor represents a bounded architecture, which builds up from heterogeneous processor cores. From the marketing point of view, the Cell processor failed, but its significant innovations (e.g.: heterogeneous processor cores, ring bus structure) can be observed in todays modern processors (e.g.: IBM Power 7 [13], Intel Sandy Bridge [14]). According to the special requirement of the processor, vectorized datas were used which composed of floating point numbers. For the development of the software the freely available IBM software development kit (SDK) with C programming language was used. Xilinx FPGAs are belonging to the leading reconfigurable computers since a while. Due to the fast Configurable Logic Blocks (CLB) and to the large number of interconnections arbitrary circuits can be implemented on them. In order to accelerate certain operations, dedicated elements (e.g.: digital signal processing (DSP) blocks) are available on the FPGA. The FPGA’s CLB and DSP can be treated like different type of processors which can handle different operations efficiently. Due to the configurable parameters of the FPGA the processed data
3 can be represented in arbitrary type and size. During the research fixed point and floating point number arithmetic units with different mantissa width were investigated in order to find the optimal precision for a qualitative good result. During the implementation process I used the Xilinx Foundation ISE softwares with VHSIC hardware description language (VHDL) language. For the software simulation I used the MentorGraphics Modelsim SE software. The Chapter 1 is built up as follows. First the CNN paradigm is introduced. It is capable to solve complex spatio-temporal problems. The standard CNN cell should be extended with a control unit and with memory units in order to get an universal machine, which is based on the stored programmability (the extension is introduced in Chapter 4). Several studies proved the effectiveness of the CNN-UM solution of different PDEs [15, 16]. After the previous section several implementations of the CNN-UM principle are listed. The emulated digital CNN-UM is a reasonable alternative to solve different PDEs. It has mixed the benefits of the software simulation and the analog solution. Namely the high configurability and the precision of the software solution and the high computing speed of the analog solution. In the later Chapters the CNN simulation kernel is going to be introduced on the IBM Cell processor and on the FPGA. In order to get the reader a clear understanding from the framework a brief introduction of the hardware specifications are presented. The final section is an outlook of the recent trends in many core systems.
Page 1: An Optimal Mapping of Numerical Sim
Page 5 and 6: Acknowledgements It is not so hard
Page 7 and 8: Contents 1 Introduction 1 1.1 Cellu
Page 9: CONTENTS iii 4.3.3 Operating Steps
Page 12 and 13: vi LIST OF FIGURES 2.4 Performance
Page 15: List of Tables 2.1 Comparison of di
Page 18 and 19: xii LIST OF ABBREVIATIONS FPE Falco
Page 21: Chapter 1 Introduction Due to the r
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2.3 Computational Fluid Flow Simula
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Chapter 3 Investigating the Precisi
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3.2 Numerical Solutions of the PDEs
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3.3 Testing Methodology 73 In fixed
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3.4 Properties of the Arithmetic Un
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3.4 Properties of the Arithmetic Un
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3.5 Results 79 in the L2 cache of t
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3.5 Results 81 1E-02 1E-03 Error 1E
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3.5 Results 83 arithmetic unit requ
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3.6 Conclusion 85 point and the 40
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4. IMPLEMENTING A GLOBAL ANALOGIC P
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110 5. SUMMARY OF NEW SCIENTIFIC RE
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References The author’s journal p
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5.2 Application of the results 123
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PPKE ITK PhD and MPhil Thesis Classes

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