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+\documentclass{article}
+\usepackage[margin=1in]{geometry}
+\usepackage[skip=0.2\baselineskip]{caption}
+\usepackage{longtable}
+\usepackage{booktabs}
+\usepackage{graphicx}
+\title{High Performance Computer Architecture Final Project}
+\author{Danila Fedorin}
+
+\begin{document}
+\maketitle
+\section*{Part 1: Address Prediction Benchmarks}
+In this part, the \emph{Taken}, \emph{Not Taken},
+\emph{Bimodal}, \emph{2-Level} and \emph{Combined} branch
+predictors were run against three benchmarks. The results
+are recorded in Figure \ref{fig:ap1}. Figure \ref{fig:ap1graph}
+provides a bar chart of this data.
+Results are grouped by benchmark to make it easier to compare
+various branch prediction algorithms.
+
+\begin{figure}[h]
+\begin{longtable}[]{@{}llllll@{}}
+\toprule
+Benchkmark & Taken & Not Taken & Bimod & 2 level &
+Combined\tabularnewline
+\midrule
+\endhead
+Anagram & .3126 & .3126 & .9613 & .8717 & .9742\tabularnewline
+GCC & .4049 & .4049 & .8661 & .7668 & .8793\tabularnewline
+Go & .3782 & .3782 & .7822 & .6768 & .7906\tabularnewline
+\bottomrule
+\end{longtable}
+\caption{Address prediction rates of various predictors}
+\label{fig:ap1}
+\end{figure}
+
+\begin{figure}[h]
+    \begin{center}
+        \includegraphics[width=0.65\linewidth]{ap1.png}
+    \end{center}
+    \caption{Address prediction rates by benchmark}
+    \label{fig:ap1graph}
+\end{figure}
+
+As expected, the two stateless predictors, \emph{Taken}
+and \emph{Not Taken}, perform significantly worse than the
+others. These predictors do not keep track of the behavior
+of various branches, and thus have limited ability
+to predict the direction of a branch. Out of the stateful
+predictors, the \emph{2-level} predictor seems to perform the worst.
+Unsurprisingly, the \emph{Combined} predictor, which is
+a combination of the other two stateful predictors, performs
+better than its constituents, since it's able to switch
+to a better-performing predictor as needed.
+
+\section*{Part 2: IPC Benchmarks}
+In this section, we present the IPC results from the previously listed
+predictors. Figure \ref{fig:ipc} contains the collected
+data, and Figure \ref{fig:ipcgraph} is a bar chart of
+that data.
+
+\begin{figure}[h]
+\begin{longtable}[]{@{}llllll@{}}
+\toprule
+Benchkmark & Taken & Not Taken & Bimod & 2 level &
+Combined\tabularnewline
+\midrule
+\endhead
+Anagram & 1.0473 & 1.0396 & 2.1871 & 1.8826 & 2.2487\tabularnewline
+GCC & 0.7878 & 0.7722 & 1.2343 & 1.1148 & 1.2598\tabularnewline
+Go & 0.9512 & 0.9412 & 1.3212 & 1.2035 & 1.3393\tabularnewline
+\bottomrule
+\end{longtable}
+\caption{IPC by benchmark}
+\label{fig:ipc}
+\end{figure}
+
+\begin{figure}[h]
+    \begin{center}
+        \includegraphics[width=0.65\linewidth]{ipc.png}
+    \end{center}
+    \caption{IPC by benchmark}
+    \label{fig:ipcgraph}
+\end{figure}
+
+Once again, the stateless predictors perform significantly
+worse than the stateful predictors. Also, \emph{Taken}
+performs better than \emph{Not Taken}. This is likely
+because most of the given programs have loops, in which
+the conditional branch is taken many times while the loop
+is iterating, and then once when the loop terminates. Predicting
+``not taken'' in this case would lead to many mispredictions.
+
+Once again, the \emph{Bimodal} predictor performs better than
+the \emph{2-Level} predictor, and both are outperform by
+\emph{Combined}, which leverages the two at the same time.
+
+\section*{Part 3 - Bimodal Exploration}
+In this section, the \emph{Bimodal} branch predictor is further
+analyzed by varying the size of the BTB. BTB sizes range from
+256 to 4096. The data collected from this analysis is shown
+in figure \ref{fig:ap2}. As usual, the data is shown as
+a bar graph in figure \ref{fig:ap2graph}.
+
+\begin{figure}[h]
+\begin{longtable}[]{@{}llllll@{}}
+\toprule
+Benchkmark & 256 & 512 & 1024 & 2048 & 4096\tabularnewline
+\midrule
+\endhead
+Anagram & .9606 & .9609 & .9612 & .9613 & .9613\tabularnewline
+GCC & .8158 & .8371 & .8554 & .8661 & .8726\tabularnewline
+Go & .7430 & .7610 & .7731 & .7822 & .7885\tabularnewline
+\bottomrule
+\end{longtable}
+\caption{Bimodal address prediction rates by benchmark}
+\label{fig:ap2}
+\end{figure}
+
+\pagebreak
+
+\begin{figure}[h]
+    \begin{center}
+        \includegraphics[width=0.65\linewidth]{ap2.png}
+    \end{center}
+    \caption{IPC by benchmark}
+    \label{fig:ap2graph}
+\end{figure}
+
+As expected, increasing the BTB size for the Bimodal
+predictor seems to improve its performance. The exception
+appears to be anagram, where the changes to performance
+are small enough to be unnoticable in the visualization.
+
+\section*{Part 4 - Combined Branch Predictor Explanation}
+It appears as though the combined branch predictor works
+by considering the decisions of both a 2-level and a bimodal 
+branch predictor. To decide which predictor to listen
+to, the combined predictor uses a third predictor, named \texttt{meta}
+in the code. The \texttt{meta} predictor appears to be another bimodal
+predictor, but instead of deciding whether a branch is taken or not
+taken, it decides whether to use the two-level or the bimodal predictor
+to determine the branch outcome. If \texttt{meta} chooses a predictor
+that ends up being wrong, while the other predictor ends up right,
+\texttt{meta}'s 2-bit counter is updated to favor the correct predictor.
+
+Because \texttt{meta} is implemented as a 2-bit predictor, it can
+tolerate at most one use of the wrong branch predictor before
+switching to the other (if the current predictor is "strongly"
+predicted).
+
+\section*{Part 5 - 3-Bit Branch Predictor}
+For this part, I modified the SimpleScalar codebase to add
+a 3-bit branch predictor. The code will be included with this
+report, but not in this document. After implementing
+this predictor, I simulated it with the same BTB sizes
+as the previous extended simulations of the Bimodal (2-bit)
+predictor. Figure \ref{fig:ap3} contains this data,
+and Figure \ref{fig:ap3graph} contains the visualization
+of that data.
+
+\begin{figure}[h]
+\begin{longtable}[]{@{}llllll@{}}
+\toprule
+Benchkmark & 256 & 512 & 1024 & 2048 & 4096\tabularnewline
+\midrule
+\endhead
+Anagram & .9610 & .9612 & .9615 & .9616 & .9616\tabularnewline
+GCC & .8192 & .8385 & .8554 & .8656 & .8728\tabularnewline
+Go & .7507 & .7680 & .7799 & .7897 & .7966\tabularnewline
+\bottomrule
+\end{longtable}
+\caption{3-Bit address prediction rates}
+\label{fig:ap3}
+\end{figure}
+
+\begin{figure}[h]
+    \begin{center}
+        \includegraphics[width=0.65\linewidth]{ap3.png}
+    \end{center}
+    \caption{3-Bit address prediction rates}
+    \label{fig:ap3graph}
+\end{figure}
+
+As with the bimodal branch predictor, the 3-bit predictor
+benefits from larger BTB sizes in the Go and GCC benchmarks,
+but seems to remain very consistent in the Anagram benchmark.
+The differences between this predictor and the related bimodal
+predictor are hard to see in this diagram.
+
+To better compare
+the two predictors, I computed the percent improvement to
+address prediction rates of the 3-bit branch predictor
+relative to the bimodal one. Figure \ref{fig:2v3} displays
+this information. From this figure, it appears as though
+the 3-bit predictor performs better than the bimodal one
+in most cases. However, it does perform slightly worse
+with a 2048-sized BTB in the GCC benchmark.
+
+The Go benchmark sees the most improvement (around 1\%).
+A 3-bit predictor performs better when branches generally
+follow the same direction, except for occasional groups
+in the other direction. If the Go benchmark implements
+the Chinese game of the same name, it's possible that the
+program behaves very much in this manner. For instance,
+if the program is scanning the board to find groups
+of ``dead'' pieces, starting at a recently placed piece,
+it will likely find pieces nearby, but occasionally run
+into empty spaces like ``eyes''. If the benchmark implements
+a Go AI, I'm not sure how it would behave computationally,
+but perhaps it also follows the same pattern.
+
+\begin{figure}[h]
+    \begin{center}
+        \includegraphics[width=0.65\linewidth]{2v3.png}
+    \end{center}
+    \caption{Percent improvement of 3-bit predictor over the bimodal predictor.}
+    \label{fig:2v3}
+\end{figure}
+
+\end{document}