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@@ -6,36 +6,24 @@ \begin{frame}\frametitle{Motivation} \begin{tcolorbox}[title=Context] - Heterogeneous datacenter architectures are common ~\cite{DBLP:conf/date/MilojicicFDR21} due to the end of Dennard's scaling~\cite{DBLP:image/48Microprocessor/Rupp}. - Today, computational needs of AI drive managers to diversify datacenters even more~\cite{DBLP:journals/computer/AthavaleBBMMPS24}. - In result datacenters become extremely complex and hard to operate. + Heterogeneous datacenter architectures are common ~\cite{DBLP:conf/date/MilojicicFDR21} and + modern computational needs of AI drive managers to diversify datacenters even more~\cite{DBLP:journals/computer/AthavaleBBMMPS24}. + In result datacenters become extremely complex and hard to operate with millions of CPU's, GPU's etc. \end{tcolorbox} - \begin{minipage}[t]{0.45\linewidth} - \tiny - \begin{center} - \includegraphics[width=0.7\linewidth]{images/nvidiagpu.png} - \end{center} - \vspace{-0.2cm} - %\textbf{Figure 1.1:} Nvidia Ada Lovelace AD102 Architecture, present in all GPUs from 2022 onwards~\cite{Wikipedia:AdaLovelaceArchi}. - %Source: official renderings by Nvidia. - %\textbf{Figure 1.1:} 48 years of microprocessor trend data. Legend: \textcolor{Orange}{$\bigblacktriangleup$ Transistors (thousands)}, \textcolor{Blue}{$\lgblkcircle$ Single Thread Performance (SpecINT $\times 10^3$)}, \textcolor{Green}{$\lgblksquare$ Frequency (MHz)}, \textcolor{Maroon}{$\bigblacktriangledown$ Typical Power (Watts)}, $\mdlgblkdiamond$ Number of Logical Cores~\cite{DBLP:image/48Microprocessor/Rupp}. - \end{minipage} - \hspace{0.5cm} - \begin{minipage}[t]{0.45\linewidth} - {\centering - \includegraphics[width=1.1\linewidth]{images/AthavaleBBMMPS24_cropped.pdf} - } - \tiny - \textbf{Figure 1.2:} Explosive growth in AI computational requirements drives datacenter upgrades (source: NVIDIA Analysis: reproduction with NVIDIA permission by~\cite{DBLP:journals/computer/AthavaleBBMMPS24}). - \end{minipage} + \begin{center} + \includegraphics[width=\linewidth]{images/datacenter_complexity.pdf} + \end{center} + \tiny + \textbf{Figure 1.1:} Society depends on datacenters to keep running, and therefore we cannot afford to let these systems break down or experience significant performance-related issues. + With millions of servers in the largest datacenters, real-time management becomes very difficult. + Left to right: a Google datacenter, server racks, Ada Lovelace AD102 GPU architecture. \end{frame} \begin{frame}\frametitle{Problem Statement} - \begin{tcolorbox}[title=We need effective tools to manage datacenters] - To address the increasing datacenter complexity, Datacenter Digital Twins (DCDT) were proposed~\cite{DBLP:journals/computer/AthavaleBBMMPS24}. - However, many DCDT's are not useful in practice, because they lack critical features (\emph{e.g.,} predictive analytics) native to the generic Digital Twin definition~\cite{DBLP:usdoe/report/AP26894}. + \begin{tcolorbox}[title=We need tools to tackle datacenter complexity] + We need Datacenter Digital Twins (DCDT) to be better able to detect and solve issues in this critical infrastructure~\cite{DBLP:journals/computer/AthavaleBBMMPS24}. + However, DCDT's are still actively developed, and lack crucial features \emph{e.g.,} predictive analytics~\cite{DBLP:usdoe/report/AP26894} to prevent unexpected job failures. \end{tcolorbox} - \input{images/table.tex} \end{frame} \begin{frame}\frametitle{Research Questions} @@ -77,7 +65,7 @@ -\setcounter{framenumber}{2} +\setcounter{framenumber}{4} \setbeamertemplate{footline}[page number]{ % Unfortunately this must remain here. |