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\section*{Coursework 5}

\noindent This coursework is worth 25\% and is due on \cwFIVE{} at

18:00. You are asked to implement a compiler targeting the LLVM-IR.

Be careful that this CW needs some material about the LLVM-IR

that has not been shown in the lectures and your own experiments

might be required. You can find information about the LLVM-IR at

\begin{itemize}

\item \url{https://bit.ly/3rheZYr}

\item \url{https://llvm.org/docs/LangRef.html}  

\end{itemize}  

\noindent

You can do the implementation of your compiler in any programming

language you like, but you need to submit the source code with which

you generated the LLVM-IR files, otherwise a mark of 0\% will be

awarded. You are asked to submit the code of your compiler, but also

the generated \texttt{.ll} files. You should use the lexer and parser

from the previous courseworks, but you need to make some modifications

to them for the `typed' fun-language. I will award up to 5\% if a

lexer and a parser are correctly implemented. At the end, please

package everything(!) in a zip-file that creates a directory with the

name

\begin{center}

\texttt{YournameYourFamilyname}

\end{center}

\noindent

\subsection*{Disclaimer\alert}

It should be understood that the work you submit represents your own

effort. You have not copied from anyone else. An exception is the

Scala code I showed during the lectures or uploaded to KEATS, which

you can both use. You can also use your own code from the CW~1 --

CW~4.

\subsection*{Task}

Mandelbrot program shown in Figure~\ref{mand}, and generate

corresponding code for the LLVM-IR. Unfortunately the calculations for

the Mandelbrot Set require floating point arithmetic and therefore we

cannot be as simple-minded about types as we have been so far

(remember the LLVM-IR is a fully-typed language and needs to know the

exact types of each expression). The idea is to deal appropriately

with three types, namely \texttt{Int}, \texttt{Double} and

\texttt{Void} (they are represented in the LLVM-IR as \texttt{i32},

\texttt{double} and \texttt{void}). You need to extend the lexer and

parser accordingly in order to deal with type annotations. The

Fun-language includes global constants, such as

\begin{lstlisting}[numbers=none]

  val Ymin: Double = -1.3;

  val Maxiters: Int = 1000;

\end{lstlisting}

\noindent

where you want to assume that they are `normal' identifiers, just

starting with a capital letter---all other identifiers should have

lower-case letters. Function definitions can take arguments of

type \texttt{Int} or \texttt{Double}, and need to specify a return

type, which can be \texttt{Void}, for example

\begin{lstlisting}[numbers=none]

  def foo(n: Int, x: Double) : Double = ...

  def id(n: Int) : Int = ...

  def bar() : Void = ...

\end{lstlisting}

\noindent

The idea is to record all typing information that is given

in the Fun-program, but then delay any further typing inference to

after the CPS-translation. That means the parser should

generate ASTs given by the Scala dataypes:

\begin{lstlisting}[numbers=none,language=Scala]

abstract class Exp 

abstract class BExp  

abstract class Decl 

case class Def(name: String, args: List[(String, String)],

               ty: String, body: Exp) extends Decl

case class Main(e: Exp) extends Decl

case class Const(name: String, v: Int) extends Decl

case class FConst(name: String, x: Float) extends Decl

case class Call(name: String, args: List[Exp]) extends Exp

case class If(a: BExp, e1: Exp, e2: Exp) extends Exp

case class Var(s: String) extends Exp

case class Num(i: Int) extends Exp     // integer numbers

case class FNum(i: Float) extends Exp  // floating numbers

case class ChConst(c: Int) extends Exp // char constant

case class Aop(o: String, a1: Exp, a2: Exp) extends Exp

case class Sequence(e1: Exp, e2: Exp) extends Exp

case class Bop(o: String, a1: Exp, a2: Exp) extends BExp

\end{lstlisting}

\noindent

This datatype distinguishes whether the global constant is an integer

constant or floating constant. Also a function definition needs to

record the return type of the function, namely the argument

\texttt{ty} in \texttt{Def}, and the arguments consist of an pairs of

identifier names and types (\texttt{Int} or \texttt{Double}). The hard

part of the CW is to design the K-intermediate language and infer all

necessary types in order to generate LLVM-IR code. You can check

your LLVM-IR code by running it with the interpreter \texttt{lli}.

\begin{figure}[t]

\lstinputlisting[language=Scala]{../progs/fun2/mand.fun}

\caption{The Mandelbrot program in the `typed' Fun-language.\label{mand}}

\end{figure}

\begin{figure}[t]

\includegraphics[scale=0.35]{../progs/fun2/out.png}

\caption{Ascii output of the Mandelbrot program.\label{mand}}

\end{figure}

Also note that the second version of the Mandelbrot program and also

the Tower of Hanoi program uses character constants, like \texttt{'a'},

\texttt{'1'}, \texttt{'$\backslash$n'} and so on. When they are tokenised,

such characters should be interpreted as the corresponding ASCII code (an

integer), such that we can use them in calculations like \texttt{'a' + 10}

where the result should be 107. As usual, the character \texttt{'$\backslash$n'} is the

ASCII code 10.

\subsection*{LLVM-IR}

There are some subtleties in the LLVM-IR you need to be aware of:

\begin{itemize}

\item \textbf{Global constants}: While global constants such as

\begin{lstlisting}[numbers=none]  

val Max : Int = 10;

\end{lstlisting}

\noindent

can be easily defined in the LLVM-IR as follows

\begin{lstlisting}[numbers=none]  

@Max = global i32 10

\end{lstlisting}

\noindent

they cannot easily be referenced. If you want to use

this constant then you need to generate code such as

\begin{lstlisting}[numbers=none]  

%tmp_22 = load i32, i32* @Max

\end{lstlisting}

\noindent

first, which treats \texttt{@Max} as an Integer-pointer (type

\texttt{i32*}) that needs to be loaded into a local variable,

here \texttt{\%tmp\_22}.

\item \textbf{Void-Functions}: While integer and double functions

  can easily be called and their results can be allocated to a

  temporary variable:

  \begin{lstlisting}[numbers=none]  

   %tmp_23 = call i32 @sqr (i32 %n)

  \end{lstlisting}

  void-functions cannot be allocated to a variable. They need to be

  called just as

  \begin{lstlisting}[numbers=none]  

  call void @print_int (i32 %tmp_23)

\end{lstlisting}

\item \textbf{Floating-Point Operations}: While integer operations

  are specified in the LLVM-IR as

  \begin{lstlisting}[numbers=none,language=Scala]

  def compile_op(op: String) = op match {

    case "+" => "add i32 "

    case "*" => "mul i32 "

    case "-" => "sub i32 "

    case "==" => "icmp eq i32 "

    case "!=" => "icmp ne i32 "

    case "<=" => "icmp sle i32 " // signed less or equal

    case "<"  => "icmp slt i32 " // signed less than

  }\end{lstlisting}

  the corresponding operations on doubles are

  \begin{lstlisting}[numbers=none,language=Scala]

  def compile_dop(op: String) = op match {

    case "+" => "fadd double "

    case "*" => "fmul double "

    case "-" => "fsub double "

    case "==" => "fcmp oeq double "

    case "!=" => "fcmp one double "

    case "<=" => "fcmp ole double "   

    case "<"  => "fcmp olt double "   

  }\end{lstlisting}

\item \textbf{Typing}: In order to leave the CPS-translations

  as is, it makes sense to defer the full type-inference to the

  K-intermediate-language. For this it is good to define

  the \texttt{KVar} constructor as

\begin{lstlisting}[numbers=none,language=Scala]  

case class KVar(s: String, ty: Ty = "UNDEF") extends KVal\end{lstlisting}

  where first a default type, for example \texttt{UNDEF}, is

  given. Then you need to define two typing functions

  \begin{lstlisting}[numbers=none,language=Scala]  

    def typ_val(v: KVal, ts: TyEnv) = ???

    def typ_exp(a: KExp, ts: TyEnv) = ???

  \end{lstlisting}

  Both functions require a typing-environment that updates

  the information about what type each variable, operation

  and so on receives. Once the types are inferred, the

  LLVM-IR code can be generated. Since we are dealing only

  with simple first-order functions, nothing on the scale

  as the `Hindley-Milner' typing-algorithm is needed. I suggest

  to just look at what data is avaliable and generate all

  missing information by ``simple means''\ldots rather than

  looking at the literature which solves the problem

  with much heavier machinery.

\item \textbf{Build-In Functions}: The `prelude' comes

  with several build-in functions: \texttt{new\_line()},

  \texttt{skip}, \texttt{print\_int(n)}, \texttt{print\_space()},

  \texttt{print\_star()} and \texttt{print\_char(n)}. You can find the `prelude' for

  example in the file \texttt{sqr.ll}.

\end{itemize}  

\end{document}

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