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\begin{document}

\begin{center}
    \pagenumbering{gobble}
    \Huge
    Sorting in X86-64 Linux Assembly
    \vspace{1cm}

    \Large
    Navid S.

    \vspace{0.5cm}
    Andreas K. L.

    \vspace{0.5cm}
    Mikkel T.

    \vspace{1cm}
    \large
    October 31$^\text{st}$ 2025
\end{center}

\newpage
\tableofcontents
\newpage

\pagenumbering{arabic}
\section{Introduction}
Sorting is one of the most fundamental operations in programming. Sorting
algorithms play a crucial role in efficient searching, organization,
interpretation of data, and optimization problems. Furthermore, sorting is often
the first step towards designing complicated algorithms in computer
science.
\smallskip

\noindent
This project aims to study efficient sorting of a list of coordinates,
when sorting by the second coordinate. This will be done entirely in
\textit{x86\_64 Linux Assembly}. Although abstract high level languages such as
\textit{Python} and \textit{Java} allow faster and convenient development,
Assembly provides precise control over program flow, memory usage and
instruction level execution, making Assembly an excellent environment for
benchmarking and performance analysis.
\smallskip

\noindent
Two sorting algorithms \textit{quicksort} and \textit{insertionsort} will be implemented and
compared against each other. Several test instances consisting of uniformly random coordinates will be
created with the goal of comparing the implementations actual runtime to their
theoretical runtimes and evaluate each implementation.

\section{Design}

\subsection{Program Flow}
The program is structured into four main phases:

\noindent
\textbf{selecting} an algorithm, \textbf{converting} the inputfile to the array format,
\textbf{sorting} that array, and \textbf{printing} the array to \textit{stdout}.
\smallskip

\noindent
Each phase is designed to handle a specific task, which
creates modularity and clarity. Within each \textit{phase} a set of functions manage the
corresponding operations, allowing the programmer to focus on one task at a
time. This design also greatly supports \textit{debugging} by simplifying tracking and
solving errors.
\smallskip

\noindent
The flowchart below visualize each step.

\begin{figure}[H]
    \centering
    \includegraphics[width=\linewidth]{Design.drawio.png}
    \caption{Program flow}
    \label{fig:flowchart}
\end{figure}

\noindent
\textbf{Parsing the data:} After reading the input, the received file is to be
\textit{parsed}, meaning that only useful data will be extracted. This will in turn
significantly simplifies \textit{iteration} through the data, by ensuring that only
meaningful are left.
\smallskip

\noindent
Lastly an important design choice has been creating an
array of \textit{pointers} to each coordinate pair. This design is explained in further
detail later. 
\medskip

\noindent
\textbf{Sorting:} After parsing, a sorting algorithm of choice can be used on
the coordinates, to sort by a preferred coordinate. The chosen algorithm can be
passed as a \textit{command line argument}, otherwise a default algorithm will be run.
\smallskip

\noindent
This design where an algorithm can be passed to the sorting function, allows the
programmed to be extended with new algorithms if needed.
\smallskip

\noindent
\textbf{Generating output:} The last step of the program is converting the
parsed data back into a format that can be printed.

\subsection{Input and Output format}
Each line contains one and only one coordinate consisting of two values
formatted as \texttt{$\langle$x$\rangle$\textbackslash
t$\langle$y$\rangle$\textbackslash n}, where \texttt{x} and \texttt{y} are
non-negative integers in the range 0 to 32767.
\smallskip

\noindent
Although limiting, this range is
a design decision taken in order to avoid unnecessary complexity and keep focus
on the main objective of the project, efficient sorting. Including negative
numbers would for example require extra logic for parsing, comparison,
converting back and forth to ASCII, and allocating memory.
\smallskip

\subsection{Selecting algorithms}
The ability to choose which algorithm to use can be quite beneficial, if you
know certain aspects about your data. Therefore a design choice has been allowing
the user to select an algorithm as a command line argument, rather than hard coding
it.
\smallskip

\noindent
This design decision is great for flexibility, and also supports future exceptions
of the program.

\subsection{Why Insertionsort and Quicksort?}
Insertionsort and Quicksort have been handpicked to research the trade-offs
between algorithmic simplicity and computational efficiency. Furthermore,
these algorithms are widely different in nature.
\smallskip

\noindent
Insertionsort is an iterative algorithm, depending primarily on memory usage,
registers, and program flow,
\smallskip

\noindent
while Quicksort is a recursive algorithm using the divide and conquer approach.
This is interesting, because Quicksort is expected to excel on large random
datasets, while Insertionsort is more suitable for small or nearly sorted
cases, which is another great motivation to understand what is really
happening when these algorithms are executed, and how the dataset affects them.

\begin{wrapfigure}[15]{r}{0.5\textwidth}
    \centering
    \begin{tikzpicture}
        \draw (0,0) rectangle (4,1); % outer box
        \node at (-0.5,0.5) {$A=$};

        % vertical dividers
        \draw (1,0) -- (1,1);
        \draw (2,0) -- (2,1);
        \draw (3,0) -- (3,1);
        \node at (0.5,0.5) {$p_1$};
        \node at (1.5,0.5) {$p_2$};
        \node at (2.5,0.5) {$\cdots$};
        \node at (3.5,0.5) {$p_n$};

        \draw (0.1,2) rectangle (0.8,5);
        \node at (0.5,2.5) {$e_{11}$};
        \node at (0.45,3.6) {$\vdots$};
        \node at (0.5,4.5) {$e_{1m}$};

        \draw (1.1,2) rectangle (1.8,5);
        \node at (1.5,2.5) {$e_{21}$};
        \node at (1.45,3.6) {$\vdots$};
        \node at (1.5,4.5) {$e_{2m}$};

        \draw (3.1,2) rectangle (3.8,5);
        \node at (3.5,2.5) {$e_{n1}$};
        \node at (3.45,3.6) {$\vdots$};
        \node at (3.5,4.5) {$e_{nm}$};

        \draw[->] (0.5,1.1) -- (0.5,1.9);
        \draw[->] (1.5,1.1) -- (1.5,1.9);
        \draw[->] (3.5,1.1) -- (3.5,1.9);
    \end{tikzpicture}
    \caption{The format of the array $A$, where $p_n$ is the $n^\text{th}$
    memory address, pointing to an array where $e_{nm}$ is the $m^\text{th}$
    element of the $n^\text{th}$ array in $A$.}
    \label{fig:array}
\end{wrapfigure}
\subsection{Sorting Format}
In order to be a little more general, and make our program easily expandable to
different types of data, we wanted to create our own array datatype, that
allows us to sort something more abstract, with respect to a key in that
abstract object.
\smallskip

\noindent
To do this we settled on an array of memory addresses, that
point to where the abstract objects are, see \autoref{fig:array}.
\smallskip

\noindent
These objects would in practice also be represented as arrays.
With this we can specify which index (key) in the sub-arrays we want to sort by,
and then when we swap elements in the array,
we simply swap the memory addresses around. 

\section{Implementation}
This four phase design has been converted into Assembly code using the x86\_64
instruction set. Furthermore the design has been split up in modules, where each
module is a collection of closely related functions.
\smallskip

\noindent
For example \textit{array\_maker.s} contains functions meant to parse data and convert it to
the array format. All functions in this program follow the x86 calling
conventions, with a few exceptions where all registers are preserved.
\smallskip

\noindent
Below is a detailed explanation of some of the more interesting functions.

\begin{wrapfigure}{r}{0.6\textwidth}
    \textbf{Input\ \ :} File descriptor $f$\\
    \textbf{Output:} Pointer and size of array $A$\\
    \\
    \texttt{// Pointer to file data in $p$ and}\\
    \texttt{// length of that data in $n$.}\\
    $p,n\leftarrow$ create\_file\_buffer($f$)\\
    \texttt{// Pointer to pairwise data in $p$}\\
    \texttt{// and number of pairs in $n$}\\
    $p,n\leftarrow$ make\_pairwise\_data($p,n$)\\
    \texttt{// Allocate space for final array,}\\
    \texttt{// and save pointer in $A$}\\
    $A\leftarrow$ allocate($8n$)\\
    \texttt{// Put memmory address of pair $i$}\\
    \texttt{// at index $i$ in $A$}\\
    \textbf{for } $i\leftarrow 0$\textbf{ to }$n-1$\textbf{ do}\\
    \hspace*{2em}$A[i]\leftarrow$ mem\_addr($p[2i]$)\\
    \textbf{end}\\
    \textbf{return} $A$ and $n$
    \caption{Pseudocode for \texttt{array\_maker}}\label{fig:arraymaker}
\end{wrapfigure}
\subsection{Array maker}
We implemented a global function \texttt{array\_maker}, that takes the
\textit{file descriptor} as input, and returns a pointer to the start of the
array, and the number of elements.
\smallskip

\noindent
With this it then first calls a function
\texttt{create\_file\_buffer}, that reads the contents of the file and puts it
in memory. It then passes this address, and the length of the file onto the
function \texttt{make\_pairwise\_data}. 
\smallskip

\noindent
This function removes
\texttt{\textbackslash t} and \texttt{\textbackslash n}, and puts the integers
side-by-side, with space for 64-bit integers. This format is nice, because it
allows us to iterate over the number of coordinates, and select every pair $i$
by going to position $2i$, and using that as the place in memory where the
$i^\text{th}$ pair in the array points to.
\smallskip

\noindent
The psudocode for this can be seen on
\autoref{fig:arraymaker}.\footnote{The assembly code is quite easy to interpret
from this psudocode, hence the psudocode instead of assembly. This also makes it
easier to understand.}

\newpage

\begin{wrapfigure}[22]{r}{0.6\textwidth}
    \snippetC{1}{22}{qsort.c}
    \caption{Pseudo code for quick-sort algorithm in c-style}\label{fig:qsort}
\end{wrapfigure}
\subsection{Quicksort}
The quicksort implementation uses \textbf{Hoare partition scheme}, 
a \textit{two-way partitioning} approach two pointers scan from opposite ends 
of the array toward the middle.
\smallskip

\noindent
The left pointer advances rightward while 
pointing at elements smaller than the \textit{pivot}, and the right pointer 
advances leftward while pointing at elements larger than the \textit{pivot}.
When both pointers points at misplaced elements, they swap them. 
\smallskip

\noindent
This continues until the pointers cross, effectively partitioning the array 
into two segments.
\medskip

\noindent
In order to compare two values in the array. The algorithm \textit{dereferences} 
the sub-array, and access the element at the given \textit{index}. 
\smallskip

\noindent
To swap two \textit{sub-arrays}, it simply swap their \textit{pointers} 
in the pointer-array.

\newpage

\subsection{Integer to ASCII}
\begin{wrapfigure}[11]{r}{0.4\textwidth}
    \begin{tabular}{|c|c|c|c|c|}
        \hline
        \multicolumn{5}{|c|}{ASCII table :: Numeric Glyphs} \\
        \hline
        0 & 1 & 2 & 3 & 4 \\
        \hline
        $48_{10}$ & $49_{10}$ &$ 50_{10}$ & $51_{10}$ &$ 52_{10}$ \\
        $30_{16}$ & $31_{16}$ & $32_{16}$ & $33_{16}$ & $34_{16}$ \\
        \hline 5 & 6 & 7 & 8 & 9 \\
        \hline
        $53_{10}$ &$ 54_{10}$ & $55_{10}$ &$ 56_{10}$ & $57_{10}$ \\
        $35_{16}$ & $36_{16}$ & $37_{16}$ & $38_{16}$ & $39_{16}$ \\
        \hline
    \end{tabular}
    \caption{Table over numeric ASCII codes}\label{fig:ascii-table}
\end{wrapfigure}

When converting each coordinate back into \textit{ASCII},
we use the \textbf{Euclidean algorithm} to extract each digit,
but the result is still in binary.
\smallskip

\noindent
To convert the binary digit to its \textit{ASCII} character,
we simply add the \textit{ASCII} code for \texttt{0},
which is \texttt{48} in decimal and \texttt{30} in hexadecimal.
This works because each digit is in order in the \textit{ASCII Table}.
\smallskip

\noindent
But the algorithm has the downside of getting each digit
in \textit{reverse} order, means we need to start right side of the \textit{buffer} when
adding new digits, as to not \textit{overwrite} previous digits.
\smallskip

\noindent
So each digit needs to be shifted down to \textit{leftmost} position in memory.
\bigskip

\noindent
To do this we used \texttt{REP MOVSB} which is a string operation in the
\textit{x86 instructionset architecture} that performs a
\textit{repeated byte copy operation}.

\noindent
\begin{wrapfigure}[8]{l}{0.55\textwidth}
    \begin{tabular}{|c|c|}
        \hline
        \multicolumn{2}{|c|}{\texttt{REP MOVSB} :: Operands Overview} \\
        \hline
        Register/Input & Purpose \\
        \hline
        \texttt{RSI} & Source memory addresse \\
        \texttt{RDI} & Destination memory addresse \\
        \texttt{RCX} & Repeat Count \\
        \texttt{DF} & Direction \\
        \hline
    \end{tabular}
    \caption{Table over \texttt{MOVSB} operands}\label{fig:movsb-operands}
\end{wrapfigure}

\noindent
It copies data from the memory location pointed to by
the \textit{source} index register (\texttt{RSI}) to the location pointed to by
the \textit{destination} index register (\texttt{RDI}),
repeating this operation for a number of \textit{iterations} specified by
the count register (\texttt{RCX}).
\vspace{1cm}

\noindent
\texttt{RSI} and \texttt{RDI} are automatically either \textit{incremented} or
\textit{decremented}, based on direction flag (\texttt{DF});
when the direction flag is \textit{clear}, both pointers \textit{increment},
and when it's \textit{set} both pointers \textit{decrement}.
\smallskip

\noindent
This \textit{instruction} is particularly useful for bulk memory operations
such as copying strings
as it encodes a potentially lengthy loop into a \textit{single} instruction,
thereby improving code density.

\section{Evaluation}

To evaluate whether our program works, we created three \textit{shell scripts},
one for generating test data (\textit{generate\_test\_data.sh}), one for testing
the validity of the output of the program (\textit{test.sh}) and one for testing
the execution time of the program (\textit{benchmark.sh}).
\smallskip

\noindent
The script that generates test files, generates files of size $n$ for all
$\{1000n\mid 0\leq n\leq 100\}$. The size is equivalent to the number of
coordinates. For each $n$ we also create three different kinds of test files.
One where all of the data is random, one where the data is already sorted, and
one where it is reversely sorted.\footnote{We also did run it for $n=$ 1 million
with random, but to get a pretty plot we decided to omit these values, so it was
not too sparse. Quicksort ran for 0.159s and Insertionsort ran for 386.935s.
This makes Quicksort roughly 2400 times faster than Insertionsort.}
\smallskip

\noindent
This way we
can see both the average case, and worst case of any algorithm we decide to use.
Now we can use our \textit{test.sh} script, which runs the program with each of
the generated test files for all sorting algorithms of the program, and compares
the output with the output from the \textit{GNU Core Utilities} program \textit{sort}.
\smallskip

\noindent
If all of the tests pass, we can then run \textit{benchmark.sh}, which times
how long it takes for our program to run with each test file for each algorithm,
and saves the result in a \textit{csv} file. The results of this \textit{csv}
file, together with functions asymptotically equivalent to $\mathcal O(n)$, $\mathcal O(n\log
n)$ and $\mathcal O(n^2)$, is shown on \autoref{fig:benchmarktime}.

\begin{figure}[H]
    \centering
    \begin{tikzpicture}
        \begin{axis}[
                xlabel={size({$n$})},
                ylabel={seconds},
                ymode=log,
                % xmode=log,
                width=\textwidth,
                legend pos=north west,
                clip mode=individual,
                mark size=1pt,
                scatter/classes={
                    isort_random={mark=*,red},
                    isort_reverse={mark=*,magenta},
                    isort_sorted={mark=*,pink},
                    qsort_random={mark=*,teal},
                    qsort_reverse={mark=*,blue},
                    qsort_sorted={mark=*,cyan}
                }
            ]

        % -- Scatter plot --
        \addplot[scatter, only marks, scatter src=explicit symbolic] table 
            [x=size, y=time, meta=type, col sep=comma]
            {../test/benchmark_results.csv};
        \legend{isort\_random, isort\_reverse, isort\_sorted, qsort\_random,
            qsort\_reverse, qsort\_sorted}

        % -- O(n)
        \addplot[domain=1:100000, samples=100, ultra thick, densely dotted, black]
          {(x * 1/18000000+0.0003)};
        \addlegendentry{$\mathcal O(n)$}

        % -- O(nlogn)
        \addplot[domain=1:100000, samples=100, ultra thick, dotted, black]
          {(x*ln(x)) * 1/80000000 + 0.0003};
        \addlegendentry{$\mathcal O(n\log n)$}

        % -- O(n^2)
        \addplot[domain=1:100000, samples=100, ultra thick, dashed, black]
          {(x*x) * 1/5000000000+0.0003};
        \addlegendentry{$\mathcal O(n^2)$}

      \end{axis}
    \end{tikzpicture}
    \caption{Benchmarking data for timing from Insertionsort and Quicksort for
    both random, sorted and reverse sorted data, plotted with a logarithmic y
    axis}\label{fig:benchmarktime}
\end{figure}
\smallskip

\noindent
From \autoref{fig:benchmarktime} we see that Insertionsort behaves as expected
with a best-case runtime of $\mathcal O(n)$ when the input is sorted, and a
worst-case runtime of $\mathcal O(n^2)$, when the input reversely sorted or
random. We also see that Quicksort behaves as expected with a best-case runtime
of $\mathcal O(n\log n)$ when the input is random, and a worst-case runtime of
$\mathcal O(n^2)$ when the input is either sorted or reversely sorted.
\smallskip

\noindent
It is however also possible to count the number of comparisons for each
algorithm. This plot can be seen on \autoref{fig:benchmarkcomp}. Here we clearly
see that, for example, Insertionsort has exactly $n$ comparisons, when sorting
and already sorted array of $n$ elements. We also see that Quicksort with random
elements, matches - within a scalar - with $n\log n$.

\begin{figure}[H]
    \centering
    \begin{tikzpicture}
        \begin{axis}[
                xlabel={size({$n$})},
                ylabel={seconds},
                ymode=log,
                % xmode=log,
                width=\textwidth,
                legend pos=south east,
                clip mode=individual,
                mark size=1pt,
                scatter/classes={
                    isort_random={mark=*,red},
                    isort_reverse={mark=*,magenta},
                    isort_sorted={mark=*,pink},
                    qsort_random={mark=*,teal},
                    qsort_reverse={mark=*,blue},
                    qsort_sorted={mark=*,cyan}
                }
            ]

        % -- Scatter plot --
        \addplot[scatter, only marks, scatter src=explicit symbolic] table 
            [x=size, y=comp, meta=type, col sep=comma]
            {../test/benchmark_comparisons.csv};
        \legend{isort\_random, isort\_reverse, isort\_sorted, qsort\_random,
            qsort\_reverse, qsort\_sorted}

        % -- O(n)
        \addplot[domain=1:100000, samples=100, ultra thick, densely dotted, black]
          {(x)};
            \addlegendentry{$n$}

        % -- O(nlogn)
        \addplot[domain=1:100000, samples=100, ultra thick, dotted, black]
          {(x*ln(x))};
        \addlegendentry{$n\log n$}

        % -- O(n^2)
        \addplot[domain=1:100000, samples=100, ultra thick, dashed, black]
          {(x*x)};
        \addlegendentry{$n^2$}

      \end{axis}
    \end{tikzpicture}
    \caption{Benchmarking data for comparisons from Insertionsort and Quicksort
    for both random, sorted and reverse sorted data. The y axis is logarithmic,
    and the reference lines are not asymptotic but
    exact.}\label{fig:benchmarkcomp}
\end{figure}

\subsection{Nonalgorithmic factors}
Although the asymptotic run times are caused by the algorithms themselves,
the total run times are also greatly affected by the concrete implementations
of highly repeated functions.
\smallskip

\noindent
A good example is our old generateOutput function, which was extremely slow.
This function has since been removed and exchanged with
a new approach using a buffer. The implementation looped over the sorted list and
used 4 write syscalls per coordinate:
\smallskip

\noindent
One for printing x-coordinate, one for
printing y, one for tab, and one for a newline character.
Thus 4 million syscalls would be needed just to print a sorted list of 1 million coordinates, which causes
an enormous program overhead.
\smallskip

\noindent
So the point is that even though the problem studied
is efficient sorting, correct usage of registers, memory and syscalls will greatly
affect and boost performance. 

\section{Conclusion}
In this project insertionsort and quicksort have been implemented in assembly x86 64 
with the goal of studying efficient sorting. Although the algorithms perform the same task,
they are quite very different.
\smallskip

\noindent
Firstly, from the benchmarks we have learned that the
algorithms are heavily affected by the dataset itself. Both algorithms have very
different run times on the sorted, reversed, and random datasets, despite the sets
having the same size.
\smallskip

\noindent
As discussed earlier, insertionsort and quicksort have roughly
the same performance on reversed datasets, insertionsort is optimal on sorted or
small datasets, and quicksort excels on random and very large datasets. Thus
efficient sorting requires an efficient algorithm well-suited for
the dataset. But it should also be mentioned that in most general cases with
random output quicksort will most likely outperform insertionsort.
\smallskip

\noindent
Furthermore, from the testings we have learned that the stable tests used on Insertionsort
cannot be used on quicksort, since quicksort doesn’t preserve the order the inputs.
This means that the algorithms might produce different results even if used
on the same dataset.
\smallskip

\noindent
Another important observation has been that although the algorithms
themselves determine the asymptotic run times, the concrete implementations also matter
for the total run times. Which means that memory usage, registers, stack management, and
syscalls should also be considered while trying to create a fast sorting algorithm.
\smallskip

\noindent
Summing all these observations up in one sentence,
we can conclude that a recipe to efficient sorting
is choosing a well suited algorithm supported by a smart implementation. 
\end{document}
