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http://hdl.handle.net/11375/11510
Title: | A Scalable Framework for Monte Carlo Simulation Using FPGA-based Hardware Accelerators with Application to SPECT Imaging |
Authors: | Kinsman, Phillip J. |
Advisor: | Nicolici, Nicola |
Department: | Electrical and Computer Engineering |
Keywords: | Monte Carlo;Hardware Acceleration;Scientific Computing;Network on Chip;FPGA;SPECT;Computer and Systems Architecture;Computer and Systems Architecture |
Publication Date: | Apr-2012 |
Abstract: | <p>As the number of transistors that are integrated onto a silicon die continues to in- crease, the compute power is becoming a commodity. This has enabled a whole host of new applications that rely on high-throughput computations. Recently, the need for faster and cost-effective applications in form-factor constrained environments has driven an interest in on-chip acceleration of algorithms based on Monte Carlo simula- tions. Though Field Programmable Gate Arrays (FPGAs), with hundreds of on-chip arithmetic units, show significant promise for accelerating these embarrassingly paral- lel simulations, a challenge exists in sharing access to simulation data amongst many concurrent experiments. This thesis presents a compute architecture for accelerating Monte Carlo simulations based on the Network-on-Chip (NoC) paradigm for on-chip communication. We demonstrate through the complete implementation of a Monte Carlo-based image reconstruction algorithm for Single-Photon Emission Computed Tomography (SPECT) imaging that this complex problem can be accelerated by two orders of magnitude on even a modestly-sized FPGA over a 2GHz Intel Core 2 Duo Processor. Futhermore, we have created a framework for further increasing paral- lelism by scaling our architecture across multiple compute devices and by extending our original design to a multi-FPGA system nearly linear increase in acceleration with logic resources was achieved.</p> |
URI: | http://hdl.handle.net/11375/11510 |
Identifier: | opendissertations/6475 7486 2321141 |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Size | Format | |
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fulltext.pdf | 2.68 MB | Adobe PDF | View/Open |
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