Static single assignment form

The primary usefulness of SSA comes from how it simultaneously simplifies and improves the results of a variety of compiler optimizations, by simplifying the properties of variables. For example, consider this piece of code:

y := 1
y := 2
x := y

Humans can see that the first assignment is not necessary, and that the value of y being used in the third line comes from the second assignment of y. A program would have to perform reaching definition analysis to determine this. But if the program is in SSA form, both of these are immediate:

y1 := 1
y2 := 2
x1 := y2

Compiler optimization algorithms which are either enabled or strongly enhanced by the use of SSA include:

• Constant propagation
• Value range propagation[4]
• Sparse conditional constant propagation
• Dead code elimination
• Global value numbering
• Partial redundancy elimination
• Strength reduction
• Register allocation

Converting ordinary code into SSA form is primarily a simple matter of replacing the target of each assignment with a new variable, and replacing each use of a variable with the "version" of the variable reaching that point. For example, consider the following control flow graph:

Changing the name on the left hand side of "x ${\displaystyle \leftarrow }$ x - 3" and changing the following uses of x to that new name would leave the program unaltered. This can be exploited in SSA by creating two new variables: x₁ and x₂, each of which is assigned only once. Likewise, giving distinguishing subscripts to all the other variables yields:

It is clear which definition each use is referring to, except for one case: both uses of y in the bottom block could be referring to either y₁ or y₂, depending on which path the control flow took.

To resolve this, a special statement is inserted in the last block, called a Φ (Phi) function. This statement will generate a new definition of y called y₃ by "choosing" either y₁ or y₂, depending on the control flow in the past.

Now, the last block can simply use y₃, and the correct value will be obtained either way. A Φ function for x is not needed: only one version of x, namely x₂ is reaching this place, so there is no problem (in other words, Φ(x₁,x₂)=x₂).

Given an arbitrary control flow graph, it can be difficult to tell where to insert Φ functions, and for which variables. This general question has an efficient solution that can be computed using a concept called dominance frontiers (see below).

Φ functions are not implemented as machine operations on most machines. A compiler can implement a Φ function by inserting "move" operations at the end of every predecessor block. In the example above, the compiler might insert a move from y₁ to y₃ at the end of the middle-left block and a move from y₂ to y₃ at the end of the middle-right block. These move operations might not end up in the final code based on the compiler's register allocation procedure. However, this approach may not work when simultaneous operations are speculatively producing inputs to a Φ function, as can happen on wide-issue machines. Typically, a wide-issue machine has a selection instruction used in such situations by the compiler to implement the Φ function.

According to Kenny Zadeck,[5] Φ functions were originally known as phony functions while SSA was being developed at IBM Research in the 1980s. The formal name of a Φ function was only adopted when the work was first published in an academic paper.

for each node b
    dominance_frontier(b) := {}
for each node b
    if the number of immediate predecessors of b ≥ 2
        for each p in immediate predecessors of b
            runner := p
            while runner ≠ idom(b)
                dominance_frontier(runner) := dominance_frontier(runner) ∪ { b }
                runner := idom(runner)

"Minimal" SSA inserts the minimal number of Φ functions required to ensure that each name is assigned a value exactly once and that each reference (use) of a name in the original program can still refer to a unique name. (The latter requirement is needed to ensure that the compiler can write down a name for each operand in each operation.)

However, some of these Φ functions could be dead. For this reason, minimal SSA does not necessarily produce the fewest Φ functions that are needed by a specific procedure. For some types of analysis, these Φ functions are superfluous and can cause the analysis to run less efficiently.

SSA form is not normally used for direct execution (although it is possible to interpret SSA[9]), and it is frequently used "on top of" another IR with which it remains in direct correspondence. This can be accomplished by "constructing" SSA as a set of functions which map between parts of the existing IR (basic blocks, instructions, operands, etc.) and its SSA counterpart. When the SSA form is no longer needed, these mapping functions may be discarded, leaving only the now-optimized IR.

Performing optimizations on SSA form usually leads to entangled SSA-Webs, meaning there are Φ instructions whose operands do not all have the same root operand. In such cases color-out algorithms are used to come out of SSA. Naive algorithms introduce a copy along each predecessor path which caused a source of different root symbol to be put in Φ than the destination of Φ. There are multiple algorithms for coming out of SSA with fewer copies, most use interference graphs or some approximation of it to do copy coalescing.[10]

Extensions to SSA form can be divided into two categories.

Renaming scheme extensions alter the renaming criterion. Recall that SSA form renames each variable when it is assigned a value. Alternative schemes include static single use form (which renames each variable at each statement when it is used) and static single information form (which renames each variable when it is assigned a value, and at the post-dominance frontier).

Feature-specific extensions retain the single assignment property for variables, but incorporate new semantics to model additional features. Some feature-specific extensions model high-level programming language features like arrays, objects and aliased pointers. Other feature-specific extensions model low-level architectural features like speculation and predication.

SSA form is a relatively recent development in the compiler community. As such, many older compilers only use SSA form for some part of the compilation or optimization process, but most do not rely on it. Examples of compilers that rely heavily on SSA form include:

• The ETH Oberon-2 compiler was one of the first public projects to incorporate "GSA", a variant of SSA.
• The LLVM Compiler Infrastructure uses SSA form for all scalar register values (everything except memory) in its primary code representation. SSA form is only eliminated once register allocation occurs, late in the compile process (often at link time).
• The Open64 compiler uses SSA form in its global scalar optimizer, though the code is brought into SSA form before and taken out of SSA form afterwards. Open64 uses extensions to SSA form to represent memory in SSA form as well as scalar values.
• As of version 4 (released in April 2005) GCC, the GNU Compiler Collection, makes extensive use of SSA. The frontends generate "GENERIC" code which is then converted into "GIMPLE" code by the "gimplifier". High-level optimizations are then applied on the SSA form of "GIMPLE". The resulting optimized intermediate code is then translated into RTL, on which low-level optimizations are applied. The architecture-specific backends finally turn RTL into assembly language.
• IBM's open source adaptive Java virtual machine, Jikes RVM, uses extended Array SSA, an extension of SSA that allows analysis of scalars, arrays, and object fields in a unified framework. Extended Array SSA analysis is only enabled at the maximum optimization level, which is applied to the most frequently executed portions of code.
• In 2002, researchers modified IBM's JikesRVM (named Jalapeño at the time) to run both standard Java bytecode and a typesafe SSA (SafeTSA) bytecode class files, and demonstrated significant performance benefits to using the SSA bytecode.
• Oracle's HotSpot Java Virtual Machine uses an SSA-based intermediate language in its JIT compiler.[11]
• Microsoft Visual C++ compiler backend available in Microsoft Visual Studio 2015 Update 3 uses SSA [12]
• Mono uses SSA in its JIT compiler called Mini.
• jackcc is an open-source compiler for the academic instruction set Jackal 3.0. It uses a simple 3-operand code with SSA for its intermediate representation. As an interesting variant, it replaces Φ functions with a so-called SAME instruction, which instructs the register allocator to place the two live ranges into the same physical register.
• Although not a compiler, the Boomerang decompiler uses SSA form in its internal representation. SSA is used to simplify expression propagation, identifying parameters and returns, preservation analysis, and more.
• Portable.NET uses SSA in its JIT compiler.
• libFirm a completely graph based SSA intermediate representation for compilers. libFirm uses SSA form for all scalar register values until code generation by use of an SSA-aware register allocator.
• The Illinois Concert Compiler circa 1994[13] used a variant of SSA called SSU (Static Single Use) which renames each variable when it is assigned a value, and in each conditional context in which that variable is used; essentially the static single information form mentioned above. The SSU form is documented in John Plevyak's Ph.D Thesis.
• The COINS compiler uses SSA form optimizations as explained here: http://www.is.titech.ac.jp/~sassa/coins-www-ssa/english/
• The Mozilla Firefox SpiderMonkey JavaScript engine uses SSA-based IR.[14]
• The Chromium V8 JavaScript engine implements SSA in its Crankshaft compiler infrastructure as announced in December 2010
• PyPy uses a linear SSA representation for traces in its JIT compiler.
• Android's Dalvik virtual machine uses SSA in its JIT compiler.
• Android's new optimizing compiler for the Android Runtime uses SSA for its IR.
• The Standard ML compiler MLton uses SSA in one of its intermediate languages.
• LuaJIT makes heavy use of SSA-based optimizations.[15]
• The PHP and Hack compiler HHVM uses SSA in its IR.[16]
• Reservoir Labs' R-Stream compiler supports non-SSA (quad list), SSA and SSI (Static Single Information[17]) forms.[18]
• Go (1.7: for x86-64 architecture only; 1.8: for all supported architectures).[19][20]
• SPIR-V, the shading language standard for the Vulkan graphics API and kernel language for OpenCL compute API, is an SSA representation.[21]
• Various Mesa drivers via NIR, an SSA representation for shading languages.[22]
• WebKit uses SSA in its JIT compilers.[23][24]
• Swift defines its own SSA form above LLVM IR, called SIL (Swift Intermediate Language).[25][26]
• Erlang rewrote their compiler in OTP 22.0 to "internally use an intermediate representation based on Static Single Assignment (SSA)." With plans for further optimizations built on top of SSA in future releases.[27]

• Bosscher, Steven; and Novillo, Diego. GCC gets a new Optimizer Framework. An article about GCC's use of SSA and how it improves over older IRs.
• The SSA Bibliography. Extensive catalogue of SSA research papers.
• Zadeck, F. Kenneth. "The Development of Static Single Assignment Form", December 2007 talk on the origins of SSA.
• VV.AA. "SSA-based Compiler Design" (2014)