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The Design of LLVM

Writing an LLVM IR Optimization

To give some intuition for how optimizations work, it is useful to walk through some examples. There are lots of different kinds of compiler optimizations, so it is hard to provide a recipe for how to solve an arbitrary problem. That said, most optimizations follow a simple three-part structure:

  • Look for a pattern to be transformed.
  • Verify that the transformation is safe/correct for the matched instance.
  • Do the transformation, updating the code.

The most trivial optimization is pattern matching on arithmetic identities, such as: for any integer X, X-X is 0, X-0 is X, (X*2)-X is X. The first question is what these look like in LLVM IR. Some examples are:

⋮    ⋮    ⋮
%example1 = sub i32 %a, %a
⋮    ⋮    ⋮
%example2 = sub i32 %b, 0
⋮    ⋮    ⋮
%tmp = mul i32 %c, 2
%example3 = sub i32 %tmp, %c
⋮    ⋮    ⋮

For these sorts of "peephole" transformations, LLVM provides an instruction simplification interface that is used as utilities by various other higher level transformations. These particular transformations are in the SimplifySubInst function and look like this:

// X - 0 -> X
if (match(Op1, m_Zero()))
  return Op0;

// X - X -> 0
if (Op0 == Op1)
  return Constant::getNullValue(Op0->getType());

// (X*2) - X -> X
if (match(Op0, m_Mul(m_Specific(Op1), m_ConstantInt<2>())))
  return Op1;


return 0;  // Nothing matched, return null to indicate no transformation.

In this code, Op0 and Op1 are bound to the left and right operands of an integer subtract instruction (importantly, these identities don't necessarily hold for IEEE floating point!). LLVM is implemented in C++, which isn't well known for its pattern matching capabilities (compared to functional languages like Objective Caml), but it does offer a very general template system that allows us to implement something similar. The match function and the m_ functions allow us to perform declarative pattern matching operations on LLVM IR code. For example, the m_Specific predicate only matches if the left hand side of the multiplication is the same as Op1.

Together, these three cases are all pattern matched and the function returns the replacement if it can, or a null pointer if no replacement is possible. The caller of this function (SimplifyInstruction) is a dispatcher that does a switch on the instruction opcode, dispatching to the opcode helper functions. It is called from various optimizations. A simple driver looks like this:

for (BasicBlock::iterator I = BB->begin(), E = BB->end(); I != E; ++I)
  if (Value *V = SimplifyInstruction(I))

This code simply loops over each instruction in a block, checking to see if any of them simplify. If so (because SimplifyInstruction returns non-null), it uses the replaceAllUsesWith method to update anything in the code using the simplifiable operation with the simpler form.

LLVM's Implementation of Three-Phase Design

In an LLVM-based compiler, a front end is responsible for parsing, validating and diagnosing errors in the input code, then translating the parsed code into LLVM IR (usually, but not always, by building an AST and then converting the AST to LLVM IR). This IR is optionally fed through a series of analysis and optimization passes which improve the code, then is sent into a code generator to produce native machine code, as shown in Figure 3. This is a very straightforward implementation of the three-phase design, but this simple description glosses over some of the power and flexibility that the LLVM architecture derives from LLVM IR.

Figure 3: LLVM's Implementation of the three-phase design.

LLVM IR is a Complete Code Representation

In particular, LLVM IR is both well specified and the only interface to the optimizer. This property means that all you need to know to write a front end for LLVM is what LLVM IR is, how it works, and the invariants it expects. Since LLVM IR has a first-class textual form, it is both possible and reasonable to build a front end that outputs LLVM IR as text, then uses UNIX pipes to send it through the optimizer sequence and code generator of your choice.

It might be surprising, but this is actually a pretty novel property to LLVM and one of the major reasons for its success in a broad range of different applications. Even the widely successful and relatively well-architected GCC compiler does not have this property: its GIMPLE mid-level representation is not a self-contained representation. As a simple example, when the GCC code generator goes to emit DWARF debug information, it reaches back and walks the source level "tree" form. GIMPLE itself uses a "tuple" representation for the operations in the code, but (at least as of GCC 4.5) still represents operands as references back to the source level tree form.

The implications of this are that front-end authors need to know and produce GCC's tree data structures as well as GIMPLE to write a GCC front end. The GCC back end has similar problems, so they also need to know bits and pieces of how the RTL back end works as well. Finally, GCC doesn't have a way to dump out "everything representing my code," or a way to read and write GIMPLE (and the related data structures that form the representation of the code) in text form. The result is that it is relatively hard to experiment with GCC, and therefore it has relatively few front ends.

LLVM is a Collection of Libraries

After the design of LLVM IR, the next most important aspect of LLVM is that it is designed as a set of libraries, rather than as a monolithic command line compiler like GCC or an opaque virtual machine like the JVM or .NET virtual machines. LLVM is an infrastructure, a collection of useful compiler technology that can be brought to bear on specific problems (like building a C compiler, or an optimizer in a special effects pipeline). While one of its most powerful features, it is also one of its least understood design points.

Let's look at the design of the optimizer as an example: it reads LLVM IR in, chews on it a bit, then emits LLVM IR, which hopefully will execute faster. In LLVM (as in many other compilers) the optimizer is organized as a pipeline of distinct optimization passes each of which is run on the input and has a chance to do something. Common examples of passes are the inliner (which substitutes the body of a function into call sites), expression reassociation, loop invariant code motion, etc. Depending on the optimization level, different passes are run: for example at -O0 (no optimization) the Clang compiler runs no passes, at -O3 it runs a series of 67 passes in its optimizer (as of LLVM 2.8).

Each LLVM pass is written as a C++ class that derives (indirectly) from the Pass class. Most passes are written in a single .cpp file, and their subclass of the Pass class is defined in an anonymous namespace (which makes it completely private to the defining file). In order for the pass to be useful, code outside the file has to be able to get it, so a single function (to create the pass) is exported from the file. Here is a slightly simplified example of a pass to make things concrete. (For more details, see Writing an LLVM Pass Manual.

namespace {
  class Hello : public FunctionPass {
    // Print out the names of functions in the LLVM IR being optimized.
    virtual bool runOnFunction(Function &F) {
      cerr << "Hello: " << F.getName() << "\n";
      return false;

FunctionPass *createHelloPass() { return new Hello(); }

As mentioned, the LLVM optimizer provides dozens of different passes, each of which are written in a similar style. These passes are compiled into one or more .o files, which are then built into a series of archive libraries (.a files on UNIX systems). These libraries provide all sorts of analysis and transformation capabilities, and the passes are as loosely coupled as possible: they are expected to stand on their own, or explicitly declare their dependencies among other passes if they depend on some other analysis to do their job. When given a series of passes to run, the LLVM PassManager uses the explicit dependency information to satisfy these dependencies and optimize the execution of passes.

Libraries and abstract capabilities are great, but they don't actually solve problems. The interesting bit comes when someone wants to build a new tool that can benefit from compiler technology, perhaps a JIT compiler for an image processing language. The implementer of this JIT compiler has a set of constraints in mind: for example, perhaps the image processing language is highly sensitive to compile-time latency and has some idiomatic language properties that are important to optimize away for performance reasons.

The library-based design of the LLVM optimizer allows our implementer to pick and choose both the order in which passes execute, and which ones make sense for the image processing domain: if everything is defined as a single big function, it doesn't make sense to waste time on inlining. If there are few pointers, alias analysis and memory optimization aren't worth bothering about. However, despite our best efforts, LLVM doesn't magically solve all optimization problems. Since the pass subsystem is modularized and the PassManager itself doesn't know anything about the internals of the passes, the implementer is free to implement their own language-specific passes to cover for deficiencies in the LLVM optimizer or to explicit language-specific optimization opportunities. Figure 4 shows a simple example for our hypothetical XYZ image processing system:

Figure 4: Hypothetical XYZ System using LLVM.

Once the set of optimizations is chosen (and similar decisions are made for the code generator) the image processing compiler is built into an executable or dynamic library. Since the only reference to the LLVM optimization passes is the simple create function defined in each .o file, and because the optimizers live in .a archive libraries, only the optimization passes that are actually used are linked into the end application, not the entire LLVM optimizer. In our aforementioned example, since there is a reference to PassA and PassB, they will get linked in. Since PassB uses PassD to do some analysis, PassD gets linked in. However, since PassC (and dozens of other optimizations) aren't used, its code isn't linked into the image processing application.

This is where the power of the library-based design of LLVM comes into play. This straightforward design approach allows LLVM to provide a vast amount of capability, some of which may only be useful to specific audiences, without punishing clients of the libraries that just want to do simple things. In contrast, traditional compiler optimizers are built as a tightly interconnected mass of code, which is much more difficult to subset, reason about, and come up to speed on. With LLVM you can understand individual optimizers without knowing how the whole system fits together.

This library-based design is also the reason why so many people misunderstand what LLVM is all about: The LLVM libraries have many capabilities, but they don't actually do anything by themselves. It is up to the designer of the client of the libraries (for example, the Clang C compiler) to decide how to put the pieces to best use. This careful layering, factoring, and focus on subset-ability is also why the LLVM optimizer can be used for such a broad range of different applications in different contexts. Also, just because LLVM provides JIT compilation capabilities, it doesn't mean that every client uses it.

Design of the Retargetable LLVM Code Generator

The LLVM code generator is responsible for transforming LLVM IR into target specific machine code. On the one hand, it is the code generator's job to produce the best possible machine code for any given target. Ideally, each code generator should be completely custom code for the target, but on the other hand, the code generators for each target need to solve very similar problems. For example, each target needs to assign values to registers, and though each target has different register files, the algorithms used should be shared wherever possible.

Similar to the approach in the optimizer, LLVM's code generator splits the code generation problem into individual passes — instruction selection, register allocation, scheduling, code layout optimization, and assembly emission — and provides many builtin passes that are run by default. The target author is then given the opportunity to choose among the default passes, override the defaults and implement completely custom target-specific passes as required. For example, the x86 back end uses a register-pressure-reducing scheduler since it has very few registers, but the PowerPC back end uses a latency optimizing scheduler since it has many of them. The x86 back end uses a custom pass to handle the x87 floating point stack, and the ARM back end uses a custom pass to place constant pool islands inside functions where needed. This flexibility allows target authors to produce great code without having to write an entire code generator from scratch for their target.

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