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Description
Welcome to 15-150’s first lab! In each lab we give you problems to work on with the assistance of the TAs. This week we cover some of the basics. Think of this lab as a first date, where you and SML are just getting to know each other a little.
We are recording attendance each week. Please show up on time! Don’t worry if you run out of time. You will still get credit for the lab, because credit is based on participation, not completeness.
1 Getting Started
1.1 Setting Your Path
If you are logged in to a cluster machine running Linux or OS X, or ssh’d to a UNIX machine, we provide a macro called smlnj that you can use in
/afs/andrew/course/15/150/bin/smlnj
To be able to conveniently use this without typing the entire path each time, you will need to modify your $PATH environment variable, as follows.
Task 1.1 First, determine which shell you are running with the command echo ${SHELL}.
- If $SHELL is /bin/bash (or something ending in bash), add the line
PATH=”/afs/andrew/course/15/150/bin:${PATH}”; export PATH
to the file .bashrc in your home directory.
To actually bring this new definition for PATH into effect, you can either start a new shell or type
source ~/.bashrc
- If $SHELL is /bin/csh (or something ending in csh), add the line
set path = ( $path /afs/andrew/course/15/150/bin/ .)
to the file .cshrc in your home directory. If you get an error when you try to run
smlnj after adding this line to your .cshrc, ask a TA to look at your .cshrc.
To actually bring this new definition for PATH into effect, you can either start a new shell or type
source ~/.cshrc
Now that you have done this, you can run SML with the command smlnj.
1.1.1 Additional SML implementations
If you want to run SML from your own machine, you have a few options. If you have SML installed locally, you just type sml. You may notice that this does not allow you to conveniently navigate the interface with the arrow keys to access other places on the line or your command history; if you want to do this, you have to run rlwrap sml, where rlwrap is part of GNU Readline. You should be able to install this with your package manager on a linux distribution, or with MacPorts on OS X. Also note that smlnj is just a macro we defined for rlwrap sml, so you may find it beneficial to do the same thing on your local machine. There are installation instructions for SML on the course website.
Keep in mind that you can access the unix machines from any machine with an internet connection by sshing to unix.andrew.cmu.edu; this may be more convenient than setting up SML to run locally.
When you run SML, you should get something that looks like:
hqbovik@unix14 hqbovik % smlnj
Standard ML of New Jersey v110.69 [built: Wed Apr 29 12:25:34 2009]
–
This is the SML REPL (read-eval-print-loop): it reads the programs you enter, evaluates
them, prints the result, and then waits for more input.
To get out of the REPL, type Control-d.
1.2 Cloning The git Repository
git is a popular version control system. We will be using a git repository to distribute all of the files for your homeworks and labs to you for this semester. It will also include compiling versions of code from lecture for you to play with, and various other helpful documents as the course progresses.
Task 1.2 Follow the instructions in Section 2 of
http://www.cs.cmu.edu/~15150/resources/git.pdf
and check out the git repository to get the files for this lab.
Once the git repository has been copied over, you should change into the code directory for this lab. Assuming you named your copy of it 15150, that means you should do
cd 15150/lab/01/code/
The whole process of checking out the git repository should look like the following transcript:
hqbovik@unix14 hqbovik % ls oldfiles private public www hqbovik@unix14 hqbovik % cd private hqbovik@unix14 private % ls
hqbovik@unix14 private % git clone /afs/andrew.cmu.edu/course/15/150/handout 15150
Initialized empty Git repository in
/afs/andrew.cmu.edu/usr12/hqbovik/private/15150/.git/
15150
hqbovik@unix14 private % cd 15150/
hqbovik@unix14 15150 % ls docs lab lecture src
hqbovik@unix14 15150 % cd lab/01/code hqbovik@unix14 code % ls
lab01.sml
Standard ML of New Jersey v110.69 [built: Wed Apr 29 12:25:34 2009]
–
2 Expressions
From here, we can type in expressions for sml/nj to evaluate. For example, if we want to add 2 + 2, we write 2 + 2;
Task 2.1 Enter the text
2 + 2;
into the REPL and press Enter. What is SML’s output?
The output line, val it = 4 : int, indicates the type and the result of evaluating the expression. it is used as a default name for the value if a name is not provided by you, 4 is the value or result, and int is the type of the expression – in this case, meaning an integer. SML uses types to ensure at compile time that programs cannot go wrong in certain ways; the phrase 4 : int can be read “4 has type int”.
Notice that the expression was terminated with a semicolon; if we do not do this, the
REPL does not know to evaluate the expression and expects more input.
Task 2.2 Enter the text
2 + 2
(no semicolon) into the REPL and press Enter. What is SML’s output? After doing that, enter a semicolon. What happens now?
As you can see, it is possible to put the semicolon on the next line and still get the same result.
2.1 Parentheses
In an arithmetic class long ago, you probably learned some standard rules of operator prece- dence – for example, multiply before you add, but anything grouped in parentheses gets evaluated first. SML follows the exact same rules of precedence. You can, of course, insert parentheses into expressions to force a particular order of evaluation.
Task 2.3 Enter the text
1 + 2 * 3 + 4;
into the REPL. What would you expect the result to be? What is the actual result? Now, enter
(1 + 2) * (3 + 4);
into the REPL. Is the result the same? Why?
3 Evaluation
As you were determining how your expressions evaluated, you may have gone step-by-step, evaluating each of the arithmetic operations one at a time. As it turns out, this is how we can determine the runtime of an expression. For example, (1 + 1) + 1 steps to 2 + 1, which steps to 3, which is a value, at which point evaluation stops. In this case, then, evaluation takes two steps to complete. For our purposes, we assume that all arithmetic operations take exactly one step of computation and numbers take zero steps. Also remember that arithmetic operators like + and * evaluate from left to right. We introduce the notation e
=⇒ e’ to mean that evaluation of e steps to e’ in a single step, and e =⇒* e’ for a finite
number of steps. So for example,
(1+1)+1 =⇒ 2+1 =⇒ 3 (1+1)+1 =⇒* 3
Task 3.1 Figure out how many steps it takes to evaluate (1 + 2) * (3 + 4) all the way, writing out each intermediate step.
3.1 Computation Trees
An important property of additions is that the final result of a bunch of additions does not depend on the order in which the adds were performed. Suppose we have some expression (…) + (…), where each parenthesized sub-expression is built from additions and numer- als. Instead of arbitrarily choosing a side to evaluate first, in a parallel setting it is possible to evaluate both sides at once, and the result will always come out the same as if we had evaluated sequentially.
It is useful to draw an expression as a computation tree (later, we will see that these expression trees are a special case of what is called a cost graph ). For example, the tree for (1 + 1) + (2 + 2) looks like:
+
/ \
+ +
/ \ / \
1 1 2 2
The leaves represent values that do not need any computation to evaluate. In this case, the only other nodes in the tree are arithmetic operations, which we have defined to have a cost of 1.
Task 3.2 Draw the tree for (1 + 2) * (3 + (4 * 5)).
3.2 Work and Span
Given an expression, we can determine its work, which is the total number of operations needed to evaluate the expression, and its span, which is the length of the longest critical path in the computation tree; a critical path is a sequence of operations that each depend on the results of the previous one. The work is the number of steps it takes to evaluate the expression sequentially, on a machine with only one processor. The span is the best possible number of steps for parallel evaluation, assuming enough processors—if there are not enough processors, the cost would be somewhere between the work and the span. We will do a bunch of work and span analysis this semester.
Once we have written the expression as a computation tree, the work is the size (number of non-leaf nodes) of the tree, and the span is the depth (length of the longest path) of the tree.
For example, the expression (1 + (2 + (3 + 4))) , with computation tree
+
/ \
1 +
/ \
2 +
/ \
3 4
has work 3, because there are three additions that need to be made. It also has a span of 3, since the result of the outermost add cannot be done until the result of the other two adds has been found, and the result of the middle add needs the result of the innermost add.
On the other hand the expression (1 + 2) + (3 + 4) has work 3 but span 2.
Task 3.3 What is the work associated with the tree for (1 + 2) * (3 + (4 * 5))?
Task 3.4 What is the span associated with the tree for (1 + 2) * (3 + (4 * 5))?
4 Types
There are more types than just int in SML. For example, there is a type string for strings of text.
Task 4.1 Enter the text
“foo”;
into the REPL. What is the result?
Instead of seeing a number as the output, you see a string here. It is possible to con- catenate two strings, using the infix ^ operator. This can be used just like + is used on integers.
Task 4.2 Enter the text
“foo” ^ ” bar”;
into the REPL. What is the result?
We can write a program that is not well-typed to see what SML does in that situation. For example, you can only concatenate two strings.
Task 4.3 What happens when you enter the expression
3 ^ 7;
into the REPL?
This is an example of one of SML’s error messages – you should start to familiarize yourselves with them, as you will be seeing them quite a lot this semester, at least until you get used to types!
5 Variables
Above, we mentioned that the results of computations are bound to the variable it by default. This means that once we have done one computation, we can refer to its result in the next computation:
Task 5.1 Enter
2 + 2;
into the REPL. Then, enter
it * 2;
into the REPL. What is the result?
As you see, before the second evaluation the value bound to it was 4 (the value of 2+2), and now it is bound to 8, the result of the most recent expression evaluation (the value of it * 2 with it bound to 4).
Of course, you shouldn’t get into the habit of using it like this! The SML runtime system only uses it (i.e. it) as a convenient default and a way to help you debug code. Usually you will want to choose a more mnemonic name by which to refer to a value, and the SML syntax for declarations allows you to do this.
A simple form of declaration has the syntax
val <varname> = <exp>
This declaration binds the value of <exp> to <varname>. If we want to mention the desired type of this variable explicitly we can say write
val <varname> : <type> = <exp>
We can use a val declaration at the prompt in the SML runtime system, or as part of a
let-expression with syntax
let val <varname>=<exp1> in <exp2> end
The value of this let-expression is obtained by evaluating <exp1>, binding its result to the variable named <varname>, and using this binding while evaluating <exp2>. This binding is not available for use outside of <exp2>, and we say that the scope of the binding is this expression.
The SML declaration syntax is much more general than we have indicated here, but this gives us enough to work with for now and also introduces the key notions of scope and binding.
Task 5.2 Enter the declaration
val x : int = 2 + 2;
into the REPL. What is the result? How does it differ from just typing 2 + 2;?
As you can see, that declaration binds the value of 2 + 2 to the variable x. We can now use the variable.
Task 5.3 Enter
x;
into the REPL; what is the result?
Task 5.4 Now enter
val y : int = x * x;
into the REPL. What is the result?
Task 5.5 How about
val y : int list = x * x;
What happens? Why?
Task 5.6 After that, enter
z * z;
into the REPL. What happens? Why?
Task 5.7 Variables in SML refer to values, but are not assignable like variables in impera- tive programming languages. Each time a variable is declared, SML creates a fresh variable and binds it to a value. This binding is available, unchanged, throughout the scope of the declaration that introduced it. If the name was already taken, the new definition shadows the previous definition: the old definition is still around, but uses of the variable refer to the new definition. We’ll talk about this more in lecture and subsequently.
Type the following in the REPL:
val x : int = 3;
val x : int = 10;
val x : string = “hello, world”;
What are the value and type of x after each line?
6 Using Files
Now that we have written some basic SML expressions, we can take a look at something a little more interesting: getting input from files. We have provided the file lab01.sml for you in the git repo you cloned in task 1.1. under lab/01/code/
Task 6.1 In the REPL, type use “lab01.sml”;. The output from SML should look like
– use “lab01.sml”; [opening lab01.sml]
…
val it = () : unit
–
Now that you have done this, you have access to everything that was defined in lab01.sml, as if you had copied and pasted the contents of the file into the REPL.
7 Functions
7.1 Applying functions
In this file, notice that there are functions defined. For example, there is
val intToString : int -> string
In this case, the function can be invoked by writing intToString(37). However, the parentheses around the argument are actually unnecessary. It doesn’t matter whether we write intToString 37 or (((intToString) (37))) – both are evaluated exactly the same.
Task 7.1 Enter
(intToString 37) ^ ” ” ^ (intToString 42);
into the REPL. What is the result?
7.2 Defining functions
We will generally require you to use a simple standard format for commenting function definitions: the SML code for a function definition should be preceded by comments that specify the function’s type, a “requires”-condition, and an “ensures”-condition that describes how the function behaves when applied to arguments that satisfy the pre-condition. We may waive this requirement when the function is part of the SML basis, or has been specified earlier. You may omit the requires-condition when it there is none (i.e. it requires true).
For example, here is a function from class, treated in this style.
(* sum : int list -> int *) (* REQUIRES: true *)
(* ENSURES: sum(L) evaluates to the sum of the integers in L. *)
fun sum [ ] = 0
| sum (x::L) = x + (sum L);
We read this specification as saying that: For all values L : int list, sum(L) evaluates to the sum of the integers in L.
Task 7.2 Complete the following template for an SML function mult that multiplies the integers in a list. Choose a sensible base case for the empty list.
(* mult : int list -> int *) (* REQUIRES: true *)
(* ENSURES: mult(L) evaluates to the product of the integers in L. *)
fun mult [ ] = (* FILL IN *)
| mult (x::L) = (* FILL IN *);
Task 7.3 Complete the specification for the following function:
(* mult’ : int list * int -> int *) (* REQUIRES: true *)
(* ENSURES: mult’(L, a) . . . (* FILL IN *) *)
fun mult’ ([ ], a) = a
| mult’ (x::L, a) = mult’ (L, x*a);
Task 7.4 Using mult, define an SML function
Mult : int list list -> int
such that for all lists of integer lists R Mult(R)
evaluates to the product of all the integers in the lists of R.
You can do this by filling in the following template:
fun Mult [ ] =
| Mult (r::R) =
Task 7.5 Using mult’, define an SML function
Mult’ : int list list * int -> int
such that for all lists of integer lists R and all integers a,
Mult’ (R, a)
evaluates to the product of a with the product of all the integers in the lists of R.
You can do this by filling in the following template:
fun Mult’ ([ ], a) =
| Mult’ (r::R, a) =
