# Background topics

This section covers a numbers of common compiler terms that arise in this guide. We try to give the general definition while providing some Rust-specific context.

## What is a control-flow graph?

A control-flow graph is a common term from compilers. If you've ever used a flow-chart, then the concept of a control-flow graph will be pretty familiar to you. It's a representation of your program that exposes the underlying control flow in a very clear way.

A control-flow graph is structured as a set of **basic blocks**
connected by edges. The key idea of a basic block is that it is a set
of statements that execute "together" – that is, whenever you branch
to a basic block, you start at the first statement and then execute
all the remainder. Only at the end of the block is there the
possibility of branching to more than one place (in MIR, we call that
final statement the **terminator**):

```
bb0: {
statement0;
statement1;
statement2;
...
terminator;
}
```

Many expressions that you are used to in Rust compile down to multiple basic blocks. For example, consider an if statement:

```
a = 1;
if some_variable {
b = 1;
} else {
c = 1;
}
d = 1;
```

This would compile into four basic blocks:

```
BB0: {
a = 1;
if some_variable { goto BB1 } else { goto BB2 }
}
BB1: {
b = 1;
goto BB3;
}
BB2: {
c = 1;
goto BB3;
}
BB3: {
d = 1;
...;
}
```

When using a control-flow graph, a loop simply appears as a cycle in
the graph, and the `break`

keyword translates into a path out of that
cycle.

## What is a dataflow analysis?

*Static Program Analysis* by Anders Møller
and Michael I. Schwartzbach is an incredible resource!

*Dataflow analysis* is a type of static analysis that is common in many
compilers. It describes a general technique, rather than a particular analysis.

The basic idea is that we can walk over a CFG and keep track of what
some value could be. At the end of the walk, we might have shown that some
claim is true or not necessarily true (e.g. "this variable must be
initialized"). `rustc`

tends to do dataflow analyses over the MIR, since that
is already a CFG.

For example, suppose we want to check that `x`

is initialized before it is used
in this snippet:

```
fn foo() {
let mut x;
if some_cond {
x = 1;
}
dbg!(x);
}
```

A CFG for this code might look like this:

```
+------+
| Init | (A)
+------+
| \
| if some_cond
else \ +-------+
| \| x = 1 | (B)
| +-------+
| /
+---------+
| dbg!(x) | (C)
+---------+
```

We can do the dataflow analysis as follows: we will start off with a flag `init`

which indicates if we know `x`

is initialized. As we walk the CFG, we will
update the flag. At the end, we can check its value.

So first, in block (A), the variable `x`

is declared but not initialized, so
`init = false`

. In block (B), we initialize the value, so we know that `x`

is
initialized. So at the end of (B), `init = true`

.

Block (C) is where things get interesting. Notice that there are two incoming
edges, one from (A) and one from (B), corresponding to whether `some_cond`

is true or not.
But we cannot know that! It could be the case the `some_cond`

is always true,
so that `x`

is actually always initialized. It could also be the case that
`some_cond`

depends on something random (e.g. the time), so `x`

may not be
initialized. In general, we cannot know statically (due to Rice's
Theorem). So what should the value of `init`

be in block (C)?

Generally, in dataflow analyses, if a block has multiple parents (like (C) in our example), its dataflow value will be some function of all its parents (and of course, what happens in (C)). Which function we use depends on the analysis we are doing.

In this case, we want to be able to prove definitively that `x`

must be
initialized before use. This forces us to be conservative and assume that
`some_cond`

might be false sometimes. So our "merging function" is "and". That
is, `init = true`

in (C) if `init = true`

in (A) *and* in (B) (or if `x`

is
initialized in (C)). But this is not the case; in particular, `init = false`

in
(A), and `x`

is not initialized in (C). Thus, `init = false`

in (C); we can
report an error that "`x`

may not be initialized before use".

There is definitely a lot more that can be said about dataflow analyses. There is an
extensive body of research literature on the topic, including a lot of theory.
We only discussed a forwards analysis, but backwards dataflow analysis is also
useful. For example, rather than starting from block (A) and moving forwards,
we might have started with the usage of `x`

and moved backwards to try to find
its initialization.

## What is "universally quantified"? What about "existentially quantified"?

In math, a predicate may be *universally quantified* or *existentially
quantified*:

*Universal*quantification:- the predicate holds if it is true for all possible inputs.
- Traditional notation: ∀x: P(x). Read as "for all x, P(x) holds".

*Existential*quantification:- the predicate holds if there is any input where it is true, i.e., there only has to be a single input.
- Traditional notation: ∃x: P(x). Read as "there exists x such that P(x) holds".

In Rust, they come up in type checking and trait solving. For example,

```
fn foo<T>()
```

This function claims that the function is well-typed for all types `T`

: `∀ T: well_typed(foo)`

.

Another example:

```
fn foo<'a>(_: &'a usize)
```

This function claims that for any lifetime `'a`

(determined by the
caller), it is well-typed: `∀ 'a: well_typed(foo)`

.

Another example:

```
fn foo<F>()
where for<'a> F: Fn(&'a u8)
```

This function claims that it is well-typed for all types `F`

such that for all
lifetimes `'a`

, `F: Fn(&'a u8)`

: `∀ F: ∀ 'a: (F: Fn(&'a u8)) => well_typed(foo)`

.

One more example:

```
fn foo(_: dyn Debug)
```

This function claims that there exists some type `T`

that implements `Debug`

such that the function is well-typed: `∃ T: (T: Debug) and well_typed(foo)`

.

## What is a DeBruijn Index?

DeBruijn indices are a way of representing which variables are bound in
which binders using only integers. They were originally invented for
use in lambda calculus evaluation. In `rustc`

, we use a similar idea for the
representation of generic types.

Here is a basic example of how DeBruijn indices might be used for closures (we
don't actually do this in `rustc`

though):

```
|x| {
f(x) // de Bruijn index of `x` is 1 because `x` is bound 1 level up
|y| {
g(x, y) // index of `x` is 2 because it is bound 2 levels up
// index of `y` is 1 because it is bound 1 level up
}
}
```

## What is co- and contra-variance?

Check out the subtyping chapter from the Rust Nomicon.

See the variance chapter of this guide for more info on how the type checker handles variance.

## What is a "free region" or a "free variable"? What about "bound region"?

Let's describe the concepts of free vs bound in terms of program variables, since that's the thing we're most familiar with.

- Consider this expression, which creates a closure:
`|a, b| a + b`

. Here, the`a`

and`b`

in`a + b`

refer to the arguments that the closure will be given when it is called. We say that the`a`

and`b`

there are**bound**to the closure, and that the closure signature`|a, b|`

is a**binder**for the names`a`

and`b`

(because any references to`a`

or`b`

within refer to the variables that it introduces). - Consider this expression:
`a + b`

. In this expression,`a`

and`b`

refer to local variables that are defined*outside*of the expression. We say that those variables**appear free**in the expression (i.e., they are**free**, not**bound**(tied up)).

So there you have it: a variable "appears free" in some expression/statement/whatever if it refers to something defined outside of that expressions/statement/whatever. Equivalently, we can then refer to the "free variables" of an expression – which is just the set of variables that "appear free".

So what does this have to do with regions? Well, we can apply the
analogous concept to type and regions. For example, in the type `&'a u32`

, `'a`

appears free. But in the type `for<'a> fn(&'a u32)`

, it
does not.

# Further Reading About Compilers

Thanks to

`mem`

,`scottmcm`

, and`Levi`

on the official Discord for the recommendations, and to`tinaun`

for posting a link to a twitter thread from Graydon Hoare which had some more recommendations!Other sources: https://gcc.gnu.org/wiki/ListOfCompilerBooks

If you have other suggestions, please feel free to open an issue or PR.

## Books

- Types and Programming Languages
- Programming Language Pragmatics
- Practical Foundations for Programming Languages
- Compilers: Principles, Techniques, and Tools, 2nd Edition
- Garbage Collection: Algorithms for Automatic Dynamic Memory Management
- Linkers and Loaders (There are also free versions of this, but the version we had linked seems to be offline at the moment.)
- Advanced Compiler Design and Implementation
- Building an Optimizing Compiler
- Crafting Interpreters