source: liacs/SCA2010/presentation1/presentation-paper-rvdzwet.tex@ 401

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Presentation about GPU nQueens paper

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1%% LyX 1.6.5 created this file. For more info, see http://www.lyx.org/.
2%% Do not edit unless you really know what you are doing.
3\documentclass[english]{beamer}
4\usepackage{mathptmx}
5\usepackage[T1]{fontenc}
6\usepackage[latin9]{inputenc}
7\usepackage{amsmath}
8\usepackage{graphicx}
9\usepackage{amssymb}
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40% or ...
41
42\setbeamercovered{transparent}
43% or whatever (possibly just delete it)
44
45\makeatother
46
47\usepackage{babel}
48
49\begin{document}
50
51
52
53
54
55\title[Chessboard Domination on GPU]{Chessboard Domination on Programmable Graphics Hardware {[}CDGPU2006{]}}
56
57
58\subtitle{{}``First algorithm to determine the minimum domination number of
59a chessboard graph using the GPU''}
60
61
62\author[Rick van der Zwet <hvdzwet@liacs.nl>]{Rick van der Zwet }
63
64
65\institute{LIACS - Leiden University}
66
67
68\date[SCA2010]{Seminar Combinatorial Algorithms, 2010}
69
70\makebeamertitle
71
72
73\pgfdeclareimage[height=0.5cm]{institution-logo}{institution-logo-filename.jpg}
74
75\logo{\pgfuseimage{institution-logo}}
76
77
78
79
80
81%\beamerdefaultoverlayspecification{<+->}
82
83
84\lyxframeend{}\lyxframe{Outline}
85
86\tableofcontents{}
87
88
89
90
91\lyxframeend{}\section{Minimum domination set}
92
93
94\lyxframeend{}\subsection[Domination set]{Domination set}
95
96
97\lyxframeend{}\lyxframe{Domination set}
98
99
100\framesubtitle{Capture them all }
101\begin{itemize}
102\item Use the least amount of items to cover a whole board
103\item Item based characteristics made whole set
104\end{itemize}
105
106\lyxframeend{}\lyxframe{Queen lower bound}
107\begin{itemize}
108\item $y(Q_{n})\geq\frac{n-1}{2}$, $n\geq1$\cite{DM2003}
109\item Every square either contains a queen, or can be reached by a queen
110(e.g. least amount of pieces required)
111\end{itemize}
112\begin{figure}
113\includegraphics[scale=0.50]{pasted1.pdf}
114\end{figure}
115
116
117\lyxframeend{}\subsection{GPU Inner Workings}
118
119
120\lyxframeend{}\lyxframe{Board Layout}
121\begin{itemize}
122\item \emph{streams:} pipelines available on the GPU - a collection of records
123requiring similar computation.
124\item \emph{kernel:} function that is applied to each element of a stream.
125\end{itemize}
126In the GPU streaming model, textures, geometry, and the framebuffer
127are seen as streams while vertex and fragment programs are seen as
128kernels.
129
130
131\lyxframeend{}\lyxframe{Outlined Figure}
132
133\begin{figure}
134\includegraphics[scale=0.75]{pasted1.png}
135\end{figure}
136
137
138\lyxframeend{}\section{Algoritm}
139
140
141\lyxframeend{}\lyxframe{Basic Algoritm}
142
143\texttt{01: finished=false}
144
145\texttt{02: do}
146
147\texttt{03: ..computes a piece configuration which may be a minimally
148dominating set }
149
150\texttt{04: ..Rendered in the framebuffer }
151
152\texttt{05: ..if (All pixels are marked) }
153
154\texttt{06: ....finished=true }
155
156\texttt{07: ..fi }
157
158\texttt{08: while (finished=false)}
159
160
161\lyxframeend{}\subsection{Computing the piece configuration}
162
163
164\lyxframeend{}\lyxframe{Method}
165\begin{itemize}
166\item Exhaustive manner
167\item Piece configuration stored on the CPU as linked links
168\item Lower bound and Upper bound is respected
169\end{itemize}
170
171\lyxframeend{}\subsection{Rendered in Framebuffer}
172\begin{itemize}
173\item GPU supports textures, every piece is a texture
174\item Render points on the CPU and offload to the GPU to map texture on
175specific place
176\end{itemize}
177\begin{figure}
178\includegraphics[scale=0.50]{pasted4.pdf}
179\end{figure}
180
181
182
183\lyxframeend{}\subsection{Determine Domination (e.g. Mark solution)}
184\begin{itemize}
185\item Simple approch
186\item Sum all pixels of $n*n$ board and match if $sum=n*n$
187\end{itemize}
188
189\lyxframeend{}\section{GPU Optimalizations}
190
191
192\lyxframeend{}\lyxframe{Colour Channels}
193\begin{figure}
194\includegraphics[scale=0.50]{pasted5.pdf}
195\end{figure}
196\begin{itemize}
197\item GPU is able to process all colours at the times
198\end{itemize}
199
200\lyxframeend{}\lyxframe{Grid Framebuffer}
201\begin{itemize}
202\item GPU has many CPU's called kernels
203\item Each kernel can process it's own little block of information
204\item Putting multiple possible solutions in one bloc
205\end{itemize}
206
207\lyxframeend{}\section{Results}
208
209
210\lyxframeend{}\subsection{Main Results}
211
212
213\lyxframeend{}\lyxframe{Conclusions and Future Work}
214
215%
216\begin{figure}
217
218
219\caption{\protect\includegraphics[scale=0.60]{pasted6}}
220
221
222Execution times (log scale) of CPU and GPU based minimum domination
223implementations computing $y(Q_{n})$. As $n$ increases, the GPU's
224speed advantage over the CPU become more evident.
225\end{figure}
226
227
228
229\lyxframeend{}\lyxframe{Conclusions and Future Work {[}2{]}}
230\begin{itemize}
231\item Domination texture good mapping between CPU world and GPU world
232\item Flexible texture definition without any impact
233\end{itemize}
234
235\lyxframeend{}\subsection{Discussion}
236
237
238\lyxframeend{}\lyxframe{Discussion}
239\begin{itemize}
240\item No significant speedup, claim that $n\geq13$ GPU is \emph{'much'
241}faster
242\item No scaleable
243\end{itemize}
244
245\lyxframeend{}\section*{Summary}
246
247
248\lyxframeend{}\lyxframe{Summary}
249\begin{itemize}
250\item First GPU algoritm for solving minimum domination described at the
251time
252\item Using texture mapping to build bridges between the CPU world and GPU
253world
254\end{itemize}
255
256
257\vskip0pt plus.5fill
258\begin{itemize}
259\item Outlook
260
261\begin{itemize}
262\item Make it scale so its decision algoritms is much smarter
263\item Build a framework to allow easy and proper testing for various combinations
264\end{itemize}
265\end{itemize}
266
267\lyxframeend{}
268
269\appendix
270
271\lyxframeend{}\section*{Appendix}
272
273
274\lyxframeend{}\subsection{For Further Reading}
275
276\beamertemplatebookbibitems
277\begin{thebibliography}{2}
278\bibitem{DM2003}E. J. Cockayne\newblock\emph{Chessboard domination
279problems}\newblock \emph{Discrete Math}., 86:1320, 1990.
280
281\bibitem{CDGPU2006}Nathan Courni\newblock\emph{Chessboard Domination
282on Programmable Graphics Hardware}\newblock \emph{ACM SE'06 March
28310-ᅵ12, 2006}. Melbourne, Florida, USA\beamertemplatearticlebibitems
284\end{thebibliography}
285
286\end{document}
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