Lesson 6: Static Single Assignment


You have undoubtedly noticed by now that many of the annoying problems in implementing analyses & optimizations stem from variable name conflicts. Wouldn’t it be nice if every assignment in a program used a unique variable name? Of course, people don’t write programs that way, so we’re out of luck. Right?

Wrong! Many compilers convert programs into static single assignment (SSA) form, which does exactly what it says: it ensures that, globally, every variable has exactly one static assignment location. (Of course, that statement might be executed multiple times, which is why it’s not dynamic single assignment.) In Bril terms, we convert a program like this:

@main {
  a: int = const 4;
  b: int = const 2;
  a: int = add a b;
  b: int = add a b;
  print b;

Into a program like this, by renaming all the variables:

@main {
  a.1: int = const 4;
  b.1: int = const 2;
  a.2: int = add a.1 b.1;
  b.2: int = add a.2 b.1;
  print b.2;

Of course, things will get a little more complicated when there is control flow. And because real machines are not SSA, using separate variables (i.e., memory locations and registers) for everything is bound to be inefficient. The idea in SSA is to convert general programs into SSA form, do all our optimization there, and then convert back to a standard mutating form before we generate backend code.


Just renaming assignments willy-nilly will quickly run into problems. Consider this program:

@main(cond: bool) {
    a: int = const 47;
    br cond .left .right;
    a: int = add a a;
    jmp .exit;
    a: int = mul a a;
    jmp .exit;
    print a;

If we start renaming all the occurrences of a, everything goes fine until we try to write that last print a. Which “version” of a should it use?

To match the expressiveness of unrestricted programs, SSA adds a new kind of instruction: a ϕ-node. ϕ-nodes are flow-sensitive copy instructions: they get a value from one of several variables, depending on which incoming CFG edge was most recently taken to get to them.

In Bril, a ϕ-node appears as a phi instruction:

a.4: int = phi .left a.2 .right a.3;

The phi instruction chooses between any number of variables, and it picks between them based on labels. If the program most recently executed a basic block with the given label, then the phi instruction takes its value from the corresponding variable.

You can write the above program in SSA like this:

@main(cond: bool) {
    a.1: int = const 47;
    br cond .left .right;
    a.2: int = add a.1 a.1;
    jmp .exit;
    a.3: int = mul a.1 a.1;
    jmp .exit;
    a.4: int = phi .left a.2 .right a.3;
    print a.4;

It can also be useful to see how ϕ-nodes crop up in loops.

(An aside: some recent SSA-form IRs, such as MLIR and Swift’s IR, use an alternative to ϕ-nodes called basic block arguments. Instead of making ϕ-nodes look like weird instructions, these IRs bake the need for ϕ-like conditional copies into the structure of the CFG. Basic blocks have named parameters, and whenever you jump to a block, you must provide arguments for those parameters. With ϕ-nodes, a basic block enumerates all the possible sources for a given variable, one for each in-edge in the CFG; with basic block arguments, the sources are distributed to the “other end” of the CFG edge. Basic block arguments are a nice alternative for “SSA-native” IRs because they avoid messy problems that arise when needing to treat ϕ-nodes differently from every other kind of instruction.)

Bril in SSA

Bril has an SSA extension. It adds support for a phi instruction. Beyond that, SSA form is just a restriction on the normal expressiveness of Bril—if you solemnly promise never to assign statically to the same variable twice, you are writing “SSA Bril.”

The reference interpreter has built-in support for phi, so you can execute your SSA-form Bril programs without fuss.

The SSA Philosophy

In addition to a language form, SSA is also a philosophy! It can fundamentally change the way you think about programs. In the SSA philosophy:

In LLVM, for example, instructions do not refer to argument variables by name—an argument is a pointer to defining instruction.

Converting to SSA

To convert to SSA, we want to insert ϕ-nodes whenever there are distinct paths containing distinct definitions of a variable. We don’t need ϕ-nodes in places that are dominated by a definition of the variable. So what’s a way to know when control reachable from a definition is not dominated by that definition? The dominance frontier!

We do it in two steps. First, insert ϕ-nodes:

for v in vars:
   for d in Defs[v]:  # Blocks where v is assigned.
     for block in DF[d]:  # Dominance frontier.
       Add a ϕ-node to block,
         unless we have done so already.
       Add block to Defs[v] (because it now writes to v!),
         unless it's already in there.

Then, rename variables:

stack[v] is a stack of variable names (for every variable v)

def rename(block):
  for instr in block:
    replace each argument to instr with stack[old name]

    replace instr's destination with a new name
    push that new name onto stack[old name]

  for s in block's successors:
    for p in s's ϕ-nodes:
      Assuming p is for a variable v, make it read from stack[v].

  for b in blocks immediately dominated by block:
    # That is, children in the dominance tree.

  pop all the names we just pushed onto the stacks


Converting from SSA

Eventually, we need to convert out of SSA form to generate efficient code for real machines that don’t have phi-nodes and do have finite space for variable storage.

The basic algorithm is pretty straightforward. If you have a ϕ-node:

v = phi .l1 x .l2 y;

Then there must be assignments to x and y (recursively) preceding this statement in the CFG. The paths from x to the phi-containing block and from y to the same block must “converge” at that block. So insert code into the phi-containing block’s immediate predecessors along each of those two paths: one that does v = id x and one that does v = id y. Then you can delete the phi instruction.

This basic approach can introduce some redundant copying. (Take a look at the code it generates after you implement it!) Non-SSA copy propagation optimization can work well as a post-processing step. For a more extensive take on how to translate out of SSA efficiently, see “Revisiting Out-of-SSA Translation for Correctness, Code Quality, and Efficiency” by Boissinot et al.