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paper.tex
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\documentclass{llncs}
%\pagestyle{plain}
\usepackage{bibnames}
\usepackage{booktabs} % For formal tables
\usepackage{caption}
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%\usepackage{subcaption}
\usepackage{listings}
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\newcommand{\myfootnote}[1]{
\renewcommand{\thefootnote}{}
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\renewcommand{\thefootnote}{\arabic{footnote}}
}
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language=C,
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showspaces=false, % show spaces everywhere adding particular underscores; it overrides 'showstringspaces'
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stepnumber=1, % the step between two line-numbers. If it's 1, each line will be numbered
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% Copyright
%\setcopyright{none}
%\setcopyright{acmcopyright}
%\setcopyright{acmlicensed}
%\setcopyright{rightsretained}
%\setcopyright{usgov}
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\begin{document}
\title{A DAG-based Programming Tool For Highly Efficient Codes on Accelerators}
\author{
Tan Nguyen,
John Bachan,
Bryce Lelbach,
Samuel Williams,
John Shalf,\\
David Donofrio, and Cy Chan}
\institute{Lawrence Berkeley National Laboratory, USA\myfootnote{Email: [email protected]. Bryce Lelbach recently moved to NVIDIA.}
}
\maketitle
\begin{abstract}
Recently, heterogeneous architectures have gained traction in HPC.
Systems with deep and complex hardware hierarchy and equipped with accelerators each having thousands of cores are increasingly commonplace.
Optimizing application codes for scalability on such hardware design is extremely challenging, since it requires effective management of the massive thread pool and the ability to balance the workload and hide communication cost among threads/accelerators.
To alleviate these labor intensive jobs, we present a solution based on task parallelism, abstracting away many hardware details associated with sophisticated accelerator architectures such as GPUs.
We also develop a runtime system called RambutanAcc that can schedule coarse and fine-grain tasks on NVIDIA's GPUs.
Using two HPC benchmarks, we demonstrate the effectiveness of our tool without intrusively modifying the source code.
\end{abstract}
\section{Introduction}
\label{sec:intro}
\input{intro}
\section{Heterogeneous Node - a Notable Design Trend}
\label{sec:motivation}
\input{motivation}
\section{Programming Model}
\label{sec:model}
\input{model}
\section{Implementation}
\label{sec:impl}
\input{impl}
\section{Experimental Results}
\label{sec:results}
\input{results}
\section{Related work}
\label{sec:related}
\input{related}
\section{Conclusion}
\label{sec:conclusion}
\input{conclusion}
\section*{Acknowledgments}
This material is based upon work supported by the Advanced Scientific Computing Research Program in the U.S. Department of Energy, Office of Science, under Award Number DE-AC02-05CH11231.
RambutanAcc and application codes were developed on TiDA, a workstation housed at LBNL, with GPUs provided by NVIDIA.
We would like to thank Paul Hargrove for his great help on GASNet.
%\input{artifact}
\bibliographystyle{splncs03}
\bibliography{ref}
\end{document}