diff --git a/performance_clusters.tex b/performance_clusters.tex
index d6f74c2..0c605a5 100644
--- a/performance_clusters.tex
+++ b/performance_clusters.tex
@@ -1,29 +1,5 @@
 \section{Performance Clusters}
 \label{sec-perf-clusters}
-\begin{figure}[t]
-    \centering
-    \includegraphics[width=\columnwidth]{./figures/plots/496/stable_line_plots/lbm_stable_lineplot_annotated_5.pdf}
-\vspace{-0.5em}
-\caption{\textbf{Stable Regions and Transitions for \textit{lbm} with
-Threshold of 5\% and Inefficiency Budget of 1.3:} Solid lines represent the
-stable regions and vertical dashed lines mark the transitions made by
-\textit{lbm}.}
-\label{lbm-stable-line-5-annotated}
-\end{figure}
-\begin{figure*}[t]
-    \begin{subfigure}[t]{\textwidth}
-	    \centering
-    	\vspace{-1em}
-    	\includegraphics[width=\columnwidth]{{./figures/plots/496/stable_line_plots/stable_lineplot}.pdf}
-    \end{subfigure}%
-\vspace{0.5em}
-\caption{\textbf{Stable Regions of \textit{gcc} and \textit{lbm} for
-Inefficiency Budget of 1.3:} Increase in cluster threshold increases the length
-of the stable regions, which eventually leads to less transitions.  Higher
-inefficiency budgets allow system to run unconstrained throughout.}
-\label{stable-regions} 
-\end{figure*}
-
 Tracking the best performance settings for a given inefficiency budget is
 expensive. In this section, we study how we can amortize the cost by trading-off
 some performance. We define the concept of \textit{performance clusters}.
@@ -51,7 +27,41 @@ We search for the performance clusters using an algorithm that is similar to the
 first filter the settings that fall within a given inefficiency budget and
 then search for the optimal settings in the first pass. In the second pass, we find all of the
 settings that have a speedup within the specified \textit{cluster threshold} of the optimal performance.
+\begin{figure}[t]
+    \centering
+    \includegraphics[width=\columnwidth]{./figures/plots/496/stable_line_plots/lbm_stable_lineplot_annotated_5.pdf}
+\vspace{-0.5em}
+\caption{\textbf{Stable Regions and Transitions for \textit{lbm} with
+Threshold of 5\% and Inefficiency Budget of 1.3:} Solid lines represent the
+stable regions and vertical dashed lines mark the transitions made by
+\textit{lbm}.}
+\label{lbm-stable-line-5-annotated}
+\end{figure}
 
+Figures~\ref{clusters-gobmk},~\ref{clusters-milc} plot the performance
+clusters during the execution of the benchmarks \textit{gobmk} and \textit{milc}. We
+plot inefficiency budgets of 1 and 1.3 and cluster thresholds of 1\% and 5\%. For
+our benchmarks, we observed that the maximum achievable inefficiency is anywhere from 1.5 to 2. We
+chose inefficiency budgets of 1 and 1.3 to cover low and mid inefficiency
+budgets. %, as energy distribution among components becomes critical to extract best performance.
+Cluster thresholds of 1\% and
+5\% allow us to model the two extremes of tolerable performance degradation bounds.
+A cluster threshold of less than 1\% may limit the ability to tune less often.
+While cluster thresholds greater than 5\% are probably not realistic as user is already
+compromising performance by setting low inefficiency budgets to save energy.
+\begin{figure*}[t]
+    \begin{subfigure}[t]{\textwidth}
+	    \centering
+    	\vspace{-1em}
+    	\includegraphics[width=\columnwidth]{{./figures/plots/496/stable_line_plots/stable_lineplot}.pdf}
+    \end{subfigure}%
+\vspace{0.5em}
+\caption{\textbf{Stable Regions of \textit{gcc} and \textit{lbm} for
+Inefficiency Budget of 1.3:} Increase in cluster threshold increases the length
+of the stable regions, which eventually leads to less transitions.  Higher
+inefficiency budgets allow system to run unconstrained throughout.}
+\label{stable-regions} 
+\end{figure*}
 \begin{figure*}[t]
     \begin{subfigure}[t]{\textwidth}
 	    \centering
@@ -81,17 +91,6 @@ stable regions increases with cluster threshold.
 \label{box-lengths}
 \end{figure*}
 
-Figures~\ref{clusters-gobmk},~\ref{clusters-milc} plot the performance
-clusters during the execution of the benchmarks \textit{gobmk} and \textit{milc}. We
-plot inefficiency budgets of 1 and 1.3 and cluster thresholds of 1\% and 5\%. For
-our benchmarks, we observed that the maximum achievable inefficiency is anywhere from 1.5 to 2. We
-chose inefficiency budgets of 1 and 1.3 to cover low and mid inefficiency
-budgets. %, as energy distribution among components becomes critical to extract best performance.
-Cluster thresholds of 1\% and
-5\% allow us to model the two extremes of tolerable performance degradation bounds.
-A cluster threshold of less than 1\% may limit the ability to tune less often.
-While cluster thresholds greater than 5\% are probably not realistic as user is already
-compromising performance by setting low inefficiency budgets to save energy.
 
 Figures~\ref{clusters-gobmk}(c),~\ref{clusters-gobmk}(d) plot the
 performance clusters for \textit{gobmk} for inefficiency budget of 1.3 and