|Version 17 (modified by simonpj, 7 years ago) (diff)|
An Integrated Code Generator for GHC
We propose reworking GHC's back end into an Integrated Code Generator, which will widen the interface between machine-independent and machine-dependent parts of the back end. We wish to dissolve the barrier between the current machine-independent transformations (CPS conversion, stack layout, etc) and the native-code generators (instruction selection, calling conventions, register allocation -- including spilling to the C stack, etc). The goal is instead to have a code generator that integrates both machine-independent and machine-dependent components, which will interact through wide but well-specified interfaces. From this refactoring we expect the following benefits:
- The back end will be simpler overall, primarily because the refactoring will reduce or eliminate duplication of code
- Complexity will be isolated in two modules with well-defined interfaces: a dataflow engine and a register allocator
- GHC will generate better machine code, primarily because important decisions about register usage will be made at a later stage of translation and will exploit knowledge of the actual target machine.
The important elements of our design are as follows:
- Build two big hammers, and hit as many nails as possible. (The big hammers are the dataflow rewriting engine and a coalescing register allocator.) The hammer itself may be big and complicated, but using a big hammer should be easy and should give easily predictable results.
- Load all back ends into every instance of the compiler, and treat every compilation as a cross-compilation. Despite having been used in production compilers for at least twenty years, this technique is still seen as somewhat unorthodox, but it removes many #ifdefs and saves significant complexity at compiler-configuration time. Removing #ifdefs also mitigates problems with validating the compiler under different build configurations.
State-of-the art dataflow optimization and register allocation both require complex implementations. We live with this complexity because creating new clients is easy.
- We can define a new optimization simply by defining a lattice of dataflow facts (akin to a specialized logic) and then writing the dataflow-transfer functions found in compiler textbooks. Handing these functions to the dataflow engine produces a new optimization that is not only useful on its own, but that can easily be composed with other optimizations to create an integrated "superoptimization" that is strictly more powerful than any sequence of individual optimizations, no matter how many times they are re-run (Lerner, Grove, and Chambers 2002).
- The back end can use fresh temporaries and register-register moves with abandon, knowing that a state-of-the-art register allocator will eliminate almost all move instructions.
- Our ultimate goal is to make adding a new back end easy as well. In the long run, we wish to apply John Dias's dissertation work to GHC. In the short run, however, we think it more sensible to represent each target-machine instruction set with an algebraic datatype. We propose to use type classes to define common functions such as identifying the registers read and written by each instruction.
Proposed compilation pipeline
- Convert from STG to an control flow graph CmmGraph (compiler/cmm/ZipCfg.hs, compiler/cmm/ZipCfgCmmRep.hs). This step is Simon PJ's "new code generator" from September 2007. This conversion may introduce new variables, stack slots, and compile-time constants.
- Implements calling conventions for call, jump, and return instructions: all parameter passing is turned into data-movement instructions (register-to-register move, load, or store), and stack-pointer adjustments are inserted. After this point, calls, returns, and jumps are just control-transfer instructions -- the parameter passing has been compiled away.
- How do we refer to locations on the stack when we haven't laid it out yet? The compiler names a stack slot using the idea of a "late compile-time constant," which is just a symbolic constant that will be replaced with an actual stack offset when the stack layout is chosen.One departure from the old code generator is that we do not build a Cmm abstract-syntax tree; instead we go straight to a control-flow graph.
In practice, we first generate an "abstract control flow graph", CmmAGraph, which makes the business of generating fresh BlockIds more convenient, and convert that to a CmmGraph. The former is convenient for construction but cannot be analysed; the latter is concrete, and can be analyzed, transformed, and optimized.
- Instruction selection: each Cmm Middle and Last node in the control-flow graph is replaced with a new graph in which the nodes are machine instructions. The MachineMiddle type represents computational machine instructions; the MachineLast type represents control-transfer instructions. The choice of representation is up to the author of the back end, but for continuity with the existing native code generators, we expect to begin by using algebraic data types inspired by the existing definitions in compiler/nativeGen/MachInstrs.hs.
- An extremely important distinction from the existing code is that we plan to eliminate #ifdef and instead provide multiple datatypes, e.g., in I386Instrs.hs, PpcInstrs.hs, SparcInstrs.hs, and so on.
We expect a set of types and an instruction selector for each back end, so for example, we might have a transformation that maps LGraph Cmm.Middle Cmm.Last (with variables, stack slots, and compile-time constants) -> LGraph I368Instrs.Middle I368Instrs.Last (with variables, stack slots, and compile-time constants)
- Optimizer: LGraph Instrs (with variables, stack slots, and compile-time constants) -> LGraph Instrs (with variables, stack slots, and compile-time constants)
- Obvious code improvements include lazy code motion and dead-code elimination as well as various optimizations based on forward substitution (copy propagation, constant propagation, peephole optimization). We intend to follow the style of Machine SUIF and make code improvements machine-independent, using machine-dependent functions to capture the semantics of instructions.
- The difficulty of implementing a good peephole optimizer varies greatly with the representation of instructions. We propose to postpone serious work on peephole optimization until we have a back end capable of representing machine instructions as RTLs, which makes peephole optimization trivial.
- Proc-point analysis: LGraph Instrs (with variables, stack slots, and compile-time constants) -> LGraph Instrs (with variables, stack slots, and compile-time constants)
- Proc points are found, and the appropriate control-transfer instructions are inserted.
- Why so early? Depending on the back end (think of C as the worst case), the proc-point analysis might have to satisfy some horrible calling convention. We want to make these requirements explicit before we get to the register allocator. We also want to exploit the register allocator to make the best possible decisions about which live variables (if any) should be in registers at a proc point.
- Register allocation: LGraph Instrs (with variables, stack slots, and compile-time constants) -> LGraph Instrs (with stack slots, and compile-time constants)
- Replace variable references with machine register and stack slots.
- Stack Layout: LGraph Instrs (with stack slots, and compile-time constants) -> LGraph Instrs
- Choose a stack layout.
- Replace references to stack slots with addresses on the stack.
- Replace compile-time constants with offsets into the stack.
- Proc-point splitting: LGraph Instrs -> [LGraph Instrs]
- Each proc point gets its own procedure.
- Code layout: LGraph Instrs -> [String]
- A reverse postorder depth-first traversal simultaneously converts the graph to sequential code and converts each instruction into an assembly-code string: Assembly code ahoy!
A key property of the design is that the scopes of machine-dependent code and machine-dependent static types are limited as much as possible:
- The representation of machine instructions may be machine-dependent (algebraic data type), or we may use a machine-independent representation that satisfies a machine-dependent dynamic invariant (RTLs). The back end should be designed in such a way that most passes don't know the difference; we intend to borrow heavily from Machine SUIF. To define the interface used to conceal the difference, Machine SUIF uses C++ classes; we will use Haskell's type classes.
- Instruction selection is necessarily machine-dependent, and moreover, it must know the representation of machine instructions
- Most of the optimizer need not know the representation of machine instructions.
- Other passes, including register allocation, stack layout, and so on, should be completely machine-independent.
- RTLs are not a new representation; they are a trivial extension of existing Cmm representations.