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Restructured the sections

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1 parent cfe412f commit 4cc9965fd26f10ff370633b5c337994099e5559d Shinpei Kato committed Apr 27, 2012
Showing with 27 additions and 22 deletions.
  1. +27 −22 draft/draft.tex
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@@ -12,51 +12,56 @@
%-----------------------------------------
% need for camera-ready
-\pagestyle{empty}
+%\pagestyle{empty}
%----------------------------------------
\begin{document}
\title{
-Supporting Energy CPS with Commodity Many-Core Devices
+Supporting Real-Time Data Communication for Commodity Many-Core Devices
}
\author {
-Shinpei Kato$^\dagger$, Nikolaus Rath$^\ddagger$, and Scott Brandt$^*$\\
+Jason Aumiller$^\dagger$, Shinpei Kato$^\ddagger$, Nikolaus Rath$^*$, and Scott Brandt$^$\\
\\
-$\dagger$ Department. of Information Engineering, Nagoya University\\
-$\ddagger$ Department of Applied Physics and Applied Mathematics, Columbia
-University \\
-$*$ Department of Computer Science, UC Santa Cruz
+$\dagger$ Department of Computer Science, UC Santa Cruz\\
+$\ddagger$ Department. of Information Engineering, Nagoya University\\
+$*$ Department of Applied Physics and Applied Mathematics, Columbia
+University
}
\maketitle
%-----------------------------------------
% need for camera-ready
-\thispagestyle{empty}
+%\thispagestyle{empty}
\begin{abstract}
- In this paper, we present two conceptual frameworks for GPU applications to
- adjust their task execution times based on total workload. These
- frameworks enable smart GPU resource management when many applications
- share GPU resources while the workloads of those applications vary.
- Application developers can explicitly adjust the number of GPU cores
- depending on their needs.
- An implicit adjustment will be supported by a run-time framwork, which
- dynamically allocates the number of cores to tasks based on the total
- workload. The runtime support of the proposed system can be realized using
- functions which measure the execution times of the tasks on GPU and change
- the number of GPU cores. We motivate the necessity of this framework
- in the context of self-driving technologies, and we believe that our
- frameworks for GPU programming are useful contributions given
- the increasing emphasis on parallel heterogeneous computing.
\end{abstract}
\section{Introduction}
\label{sec:introduction}
+\section{System Model}
+\label{sec:system_model}
+
+\section{Data Communication Methods}
+\label{sec:data_communication}
+
+\section{System Implementation}
+\label{sec:implementation}
+
+\section{Evaluation}
+\label{sec:evaluation}
+
+\section{Related Work}
+\label{sec:related_work}
+
+\section{Conclusion}
+\label{sec:conclusion}
+
+
\bibliographystyle{plain}
{\footnotesize
\bibliography{references}

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