New test.

[mono.git] / docs / mini-doc.txt

               A new JIT compiler for the Mono Project

           Miguel de Icaza (miguel@{ximian.com,gnome.org}),
           Paolo Molaro (lupus@{ximian.com,debian.org})

  
* Abstract

        Mini is a new compilation engine for the Mono runtime.  The
        new engine is designed to bring new code generation
        optimizations, portability and pre-compilation. 

        In this document we describe the design decisions and the
        architecture of the new compilation engine. 

* Introduction

        Mono is a Open Source implementation of the .NET Framework: it
        is made up of a runtime engine that implements the ECMA Common
        Language Infrastructure (CLI), a set of compilers that target
        the CLI and a large collection of class libraries.

        This article discusses the new code generation facilities that
        have been added to the Mono runtime.  

        First we discuss the overall architecture of the Mono runtime,
        and how code generation fits into it; Then we discuss the
        development and basic architecture of our first JIT compiler
        for the ECMA CIL framework.  The next section covers the
        objectives for the work on the new JIT compiler, then we
        discuss the new features available in the new JIT compiler,
        and finally a technical description of the new code generation
        engine.

* Architecture of the Mono Runtime

        The Mono runtime is an implementation of the ECMA Common
        Language Infrastructure (CLI), whose aim is to be a common
        platform for executing code in multiple languages.

        Languages that target the CLI generate images that contain
        code in high-level intermediate representation called the
        "Common Intermediate Language".  This intermediate language is
        rich enough to allow for programs and pre-compiled libraries
        to be reflected.  The execution environment allows for an
        object oriented execution environment with single inheritance
        and multiple interface implementations.

        This runtime provides a number of services for programs that
        are targeted to it: Just-in-Time compilation of CIL code into
        native code, garbage collection, thread management, I/O
        routines, single, double and decimal floating point,
        asynchronous method invocation, application domains, and a
        framework for building arbitrary RPC systems (remoting) and
        integration with system libraries through the Platform Invoke
        functionality.

        The focus of this document is on the services provided by the
        Mono runtime to transform CIL bytecodes into code that is
        native to the underlying architecture.

        The code generation interface is a set of macros that allow a
        C programmer to generate code on the fly, this is done
        through a set of macros found in the mono/jit/arch/ directory.
        These macros are used by the JIT compiler to generate native
        code. 

        The platform invocation code is interesting, as it generates
        CIL code on the fly to marshal parameters, and then this
        code is in turned processed by the JIT engine.

* Previous Experiences

        Mono has built a JIT engine, which has been used to bootstrap
        Mono since January, 2002.  This JIT engine has reasonable
        performance, and uses an tree pattern matching instruction
        selector based on the BURS technology.  This JIT compiler was
        designed by Dietmar Maurer, Paolo Molaro and Miguel de Icaza.

        The existing JIT compiler has three phases:

                * Re-creation of the semantic tree from CIL
                  byte-codes.

                * Instruction selection, with a cost-driven
                  engine. 

                * Code generation and register allocation.

        It is also hooked into the rest of the runtime to provide
        services like marshaling, just-in-time compilation and
        invocation of "internal calls". 

        This engine constructed a collection of trees, which we
        referred to as the "forest of trees", this forest is created by
        "hydrating" the CIL instruction stream.

        The first step was to identify the basic blocks on the method,
        and computing the control flow graph (cfg) for it.  Once this
        information was computed, a stack analysis on each basic block
        was performed to create a forest of trees for each one of
        them. 

        So for example, the following statement:

               int a, b;
               ...
               b = a + 1;

        Which would be represented in CIL as:

                        ldloc.0 
                        ldc.i4.1 
                        add 
                        stloc.1 

        After the stack analysis would create the following tree:

               (STIND_I4 ADDR_L[EBX|2] (
                         ADD (LDIND_I4 ADDR_L[ESI|1]) 
                         CONST_I4[1]))

        This tree contains information from the stack analysis: for
        instance, notice that the operations explicitly encode the
        data types they are operating on, there is no longer an
        ambiguity on the types, because this information has been
        inferred. 

        At this point the JIT would pass the constructed forest of
        trees to the architecture-dependent JIT compiler.  

        The architecture dependent code then performed register
        allocation (optionally using linear scan allocation for
        variables, based on life analysis).  

        Once variables had been assigned, a tree pattern matching with
        dynamic programming is used (the tree pattern matcher is
        custom build for each architecture, using a code
        generator: monoburg). The instruction selector used cost
        functions to select the best instruction patterns.  

        The instruction selector is able to produce instructions that
        take advantage of the x86 instruction indexing instructions
        for example. 

        One problem though is that the code emitter and the register
        allocator did not have any visibility outside the current
        tree, which meant that some redundant instructions were
        generated.  A peephole optimizer with this architecture was
        hard to write, given the tree-based representation that is
        used.

        This JIT was functional, but it did not provide a good
        architecture to base future optimizations on.  Also the
        line between architecture neutral and architecture
        specific code and optimizations was hard to draw.

        The JIT engine supported two code generation modes to support
        the two optimization modes for applications that host multiple
        application domains: generate code that will be shared across
        application domains, or generate code that will not be shared
        across application domains.

* Objectives of the new JIT engine.

        We wanted to support a number of features that were missing:

           * Ahead-of-time compilation.  

             The idea is to allow developers to pre-compile their code
             to native code to reduce startup time, and the working
             set that is used at runtime in the just-in-time compiler.

             Although in Mono this has not been a visible problem, we
             wanted to pro-actively address this problem.

             When an assembly (a Mono/.NET executable) is installed in
             the system, it would then be possible to pre-compile the
             code, and have the JIT compiler tune the generated code
             to the particular CPU on which the software is
             installed. 

             This is done in the Microsoft.NET world with a tool
             called ngen.exe

           * Have a good platform for doing code optimizations. 

             The design called for a good architecture that would
             enable various levels of optimizations: some
             optimizations are better performed on high-level
             intermediate representations, some on medium-level and
             some at low-level representations.

             Also it should be possible to conditionally turn these on
             or off.  Some optimizations are too expensive to be used
             in just-in-time compilation scenarios, but these
             expensive optimizations can be turned on for
             ahead-of-time compilations or when using profile-guided
             optimizations on a subset of the executed methods.

           * Reduce the effort required to port the Mono code
             generator to new architectures.

             For Mono to gain wide adoption in the Unix world, it is
             necessary that the JIT engine works in most of today's
             commercial hardware platforms. 

* Features of the new JIT engine.

        The new JIT engine was architected by Dietmar Maurer and Paolo
        Molaro, based on the new objectives.

        Mono provides a number of services to applications running
        with the new JIT compiler:

             * Just-in-Time compilation of CLI code into native code.

             * Ahead-of-Time compilation of CLI code, to reduce
               startup time of applications. 

        A number of software development features are also available:

             * Execution time profiling (--profile)

               Generates a report of the times consumed by routines,
               as well as the invocation times, as well as the
               callers.

             * Memory usage profiling (--profile)

               Generates a report of the memory usage by a program
               that is ran under the Mono JIT.

             * Code coverage (--coverage)

             * Execution tracing.

        People who are interested in developing and improving the Mini
        JIT compiler will also find a few useful routines:

             * Compilation times

               This is used to time the execution time for the JIT
               when compiling a routine. 

             * Control Flow Graph and Dominator Tree drawing.

               These are visual aids for the JIT developer: they
               render representations of the Control Flow graph, and
               for the more advanced optimizations, they draw the
               dominator tree graph. 

               This requires Dot (from the graphwiz package) and Ghostview.

             * Code generator regression tests.  

               The engine contains support for running regression
               tests on the virtual machine, which is very helpful to
               developers interested in improving the engine.

             * Optimization benchmark framework.

               The JIT engine will generate graphs that compare
               various benchmarks embedded in an assembly, and run the
               various tests with different optimization flags.  

               This requires Perl, GD::Graph.

* Flexibility

        This is probably the most important component of the new code
        generation engine.  The internals are relatively easy to
        replace and update, even large passes can be replaced and
        implemented differently.

* New code generator

        Compiling a method begins with the `mini_method_to_ir' routine
        that converts the CIL representation into a medium
        intermediate representation.

        The mini_method_to_ir routine performs a number of operations:

            * Flow analysis and control flow graph computation.

              Unlike the previous version, stack analysis and control
              flow graphs are computed in a single pass in the
              mini_method_to_ir function, this is done for performance
              reasons: although the complexity increases, the benefit
              for a JIT compiler is that there is more time available
              for performing other optimizations.

            * Basic block computation.

              mini_method_to_ir populates the MonoCompile structure
              with an array of basic blocks each of which contains
              forest of trees made up of MonoInst structures.

            * Inlining

              Inlining is no longer restricted to methods containing
              one single basic block, instead it is possible to inline
              arbitrary complex methods.

              The heuristics to choose what to inline are likely going
              to be tuned in the future.

            * Method to opcode conversion.

              Some method call invocations like `call Math.Sin' are
              transformed into an opcode: this transforms the call
              into a semantically rich node, which is later inline
              into an FPU instruction.

              Various Array methods invocations are turned into
              opcodes as well (The Get, Set and Address methods)

            * Tail recursion elimination

        Basic blocks ****

        The MonoInst structure holds the actual decoded instruction,
        with the semantic information from the stack analysis.
        MonoInst is interesting because initially it is part of a tree
        structure, here is a sample of the same tree with the new JIT
        engine:

                 (stind.i4 regoffset[0xffffffd4(%ebp)] 
                           (add (ldind.i4 regoffset[0xffffffd8(%ebp)])
                                 iconst[1]))

        This is a medium-level intermediate representation (MIR). 

        Some complex opcodes are decomposed at this stage into a
        collection of simpler opcodes.  Not every complex opcode is
        decomposed at this stage, as we need to preserve the semantic
        information during various optimization phases.  

        For example a NEWARR opcode carries the length and the type of
        the array that could be used later to avoid type checking or
        array bounds check.

        There are a number of operations supported on this
        representation:

                * Branch optimizations.

                * Variable liveness.

                * Loop optimizations: the dominator trees are
                  computed, loops are detected, and their nesting
                  level computed.

                * Conversion of the method into static single assignment
                  form (SSA form).

                * Dead code elimination.

                * Constant propagation.

                * Copy propagation.

                * Constant folding.

        Once the above optimizations are optionally performed, a
        decomposition phase is used to turn some complex opcodes into
        internal method calls.  In the initial version of the JIT
        engine, various operations on longs are emulated instead of
        being inlined.  Also the newarr invocation is turned into a
        call to the runtime.

        At this point, after computing variable liveness, it is
        possible to use the linear scan algorithm for allocating
        variables to registers.  The linear scan pass uses the
        information that was previously gathered by the loop nesting
        and loop structure computation to favor variables in inner
        loops.   This process updates the basic block `nesting' field
        which is later used during liveness analysis.

        Stack space is then reserved for the local variables and any
        temporary variables generated during the various
        optimizations.

** Instruction selection 

        At this point, the BURS instruction selector is invoked to
        transform the tree-based representation into a list of
        instructions.  This is done using a tree pattern matcher that
        is generated for the architecture using the `monoburg' tool. 

        Monoburg takes as input a file that describes tree patterns,
        which are matched against the trees that were produced by the
        engine in the previous stages.

        The pattern matching might have more than one match for a
        particular tree.  In this case, the match selected is the one
        whose cost is the smallest.  A cost can be attached to each
        rule, and if no cost is provided, the implicit cost is one.
        Smaller costs are selected over higher costs.

        The cost function can be used to select particular blocks of
        code for a given architecture, or by using a prohibitive high
        number to avoid having the rule match.

        The various rules that our JIT engine uses transform a tree of
        MonoInsts into a list of monoinsts:

        +-----------------------------------------------------------+
        | Tree                                           List       |
        | of           ===> Instruction selection ===>   of         |
        | MonoInst                                       MonoInst.  |
        +-----------------------------------------------------------+

        During this process various "types" of MonoInst kinds 
        disappear and turned into lower-level representations.  The
        JIT compiler just happens to reuse the same structure (this is
        done to reduce memory usage and improve memory locality).

        The instruction selection rules are split in a number of
        files, each one with a particular purpose:

                inssel.brg
                        Contains the generic instruction selection
                        patterns.

                inssel-x86.brg
                        Contains x86 specific rules.

                inssel-ppc.brg
                        Contains PowerPC specific rules.

                inssel-long32.brg
                        burg file for 64bit instructions on 32bit architectures.

                inssel-long.brg
                        burg file for 64bit architectures.

                inssel-float.brg
                        burg file for floating point instructions
                
        For a given build, a set of those files would be included.
        For example, for the build of Mono on the x86, the following
        set is used:

            inssel.brg inssel-x86.brg inssel-long32.brg inssel-float.brg

** Native method generation

        The native method generation has a number of steps:

                * Architecture specific register allocation.

                  The information about loop nesting that was
                  previously gathered is used here to hint the
                  register allocator. 

                * Generating the method prolog/epilog.

                * Optionally generate code to introduce tracing facilities.

                * Hooking into the debugger.

                * Performing any pending fixups. 

                * Code generation.

*** Code Generation

        The actual code generation is contained in the architecture
        specific portion of the compiler.  The input to the code
        generator is each one of the basic blocks with its list of
        instructions that were produced in the instruction selection
        phase.

        During the instruction selection phase, virtual registers are
        assigned.  Just before the peephole optimization is performed,
        physical registers are assigned.

        A simple peephole and algebraic optimizer is ran at this
        stage.  

        The peephole optimizer removes some redundant operations at
        this point.  This is possible because the code generation at
        this point has visibility into the basic block that spans the
        original trees.  

        The algebraic optimizer performs some simple algebraic
        optimizations that replace expensive operations with cheaper
        operations if possible.

        The rest of the code generation is fairly simple: a switch
        statement is used to generate code for each of the MonoInsts,
        in the mono/mini/mini-ARCH.c files, the method is called
        "mono_arch_output_basic_block".

        We always try to allocate code in sequence, instead of just using
        malloc. This way we increase spatial locality which gives a massive
        speedup on most architectures.

*** Ahead of Time compilation

        Ahead-of-Time compilation is a new feature of our new
        compilation engine.  The compilation engine is shared by the
        Just-in-Time (JIT) compiler and the Ahead-of-Time compiler
        (AOT).

        The difference is on the set of optimizations that are turned
        on for each mode: Just-in-Time compilation should be as fast
        as possible, while Ahead-of-Time compilation can take as long
        as required, because this is not done at a time critical
        time. 

        With AOT compilation, we can afford to turn all of the
        computationally expensive optimizations on.

        After the code generation phase is done, the code and any
        required fixup information is saved into a file that is
        readable by "as" (the native assembler available on all
        systems). This assembly file is then passed to the native
        assembler, which generates a loadable module.

        At execution time, when an assembly is loaded from the disk,
        the runtime engine will probe for the existence of a
        pre-compiled image.  If the pre-compiled image exists, then it
        is loaded, and the method invocations are resolved to the code
        contained in the loaded module.

        The code generated under the AOT scenario is slightly
        different than the JIT scenario.  It generates code that is
        application-domain relative and that can be shared among
        multiple thread.

        This is the same code generation that is used when the runtime
        is instructed to maximize code sharing on a multi-application
        domain scenario.

* SSA-based optimizations

        SSA form simplifies many optimization because each variable
        has exactly one definition site.  This means that each
        variable is only initialized once.  

        For example, code like this:

            a = 1
            ..
            a = 2
            call (a)

        Is internally turned into:

            a1 = 1
            ..
            a2 = 2
            call (a2)

        In the presence of branches, like:

            if (x)
                 a = 1
            else
                 a = 2

            call (a)

        The code is turned into:

            if (x)
                 a1 = 1;
            else
                 a2 = 2;
            a3 = phi (a1, a2)
            call (a3)

        All uses of a variable are "dominated" by its definition

        This representation is useful as it simplifies the
        implementation of a number of optimizations like conditional
        constant propagation, array bounds check removal and dead code
        elimination. 

* Register allocation.

        Global register allocation is performed on the medium
        intermediate representation just before instruction selection
        is performed on the method.  Local register allocation is
        later performed at the basic-block level on the 

        Global register allocation uses the following input:

        1) set of register-sized variables that can be allocated to a
        register (this is an architecture specific setting, for x86
        these registers are the callee saved register ESI, EDI and
        EBX). 

        2) liveness information for the variables

        3) (optionally) loop info to favor variables that are used in
        inner loops.

        During instruction selection phase, symbolic registers are
        assigned to temporary values in expressions.

        Local register allocation assigns hard registers to the
        symbolic registers, and it is performed just before the code
        is actually emitted and is performed at the basic block level.
        A CPU description file describes the input registers, output
        registers, fixed registers and clobbered registers by each
        operation.

* BURG Code Generator Generator

       monoburg was written by Dietmar Maurer. It is based on the
       papers from Christopher W. Fraser, Robert R. Henry and Todd
       A. Proebsting: "BURG - Fast Optimal Instruction Selection and
       Tree Parsing" and "Engineering a Simple, Efficient Code
       Generator Generator".

       The original BURG implementation is unable to work on DAGs, instead only
       trees are allowed. Our monoburg implementations is able to generate tree
       matcher which works on DAGs, and we use this feature in the new
       JIT. This simplifies the code because we can directly pass DAGs and
       don't need to convert them to trees.

* Adding IL opcodes: an excercise (from a post by Paolo Molaro)

        mini.c is the file that read the IL code stream and decides
        how any single IL instruction is implemented
        (mono_method_to_ir () func), so you always have to add an
        entry to the big switch inside the function: there are plenty
        of examples in that file.

        An IL opcode can be implemented in a number of ways, depending
        on what it does and how it needs to do it.
        
        Some opcodes are implemented using a helper function: one of
        the simpler examples is the CEE_STELEM_REF implementation.

        In this case the opcode implementation is written in a C
        function.  You will need to register the function with the jit
        before you can use it (mono_register_jit_call) and you need to
        emit the call to the helper using the mono_emit_jit_icall()
        function.  

        This is the simpler way to add a new opcode and it doesn't
        require any arch-specific change (though it's limited to what
        you can do in C code and the performance may be limited by the
        function call).
        
        Other opcodes can be implemented with one or more of the already
        implemented low-level instructions. 

        An example is the OP_STRLEN opcode which implements
        String.Length using a simple load from memory.  In this case
        you need to add a rule to the appropriate burg file,
        describing what are the arguments of the opcode and what is,
        if any, it's 'return' value.

        The OP_STRLEN case is:
        
        reg: OP_STRLEN (reg) {  
                MONO_EMIT_LOAD_MEMBASE_OP (s, tree, OP_LOADI4_MEMBASE, state->reg1, 
                        state->left->reg1, G_STRUCT_OFFSET (MonoString, length));
        }

        The above means: the OP_STRLEN takes a register as an argument
        and returns its value in a register.  And the implementation
        of this is included in the braces.
        
        The opcode returns a value in an integer register
        (state->reg1) by performing a int32 load of the length field
        of the MonoString represented by the input register
        (state->left->reg1): before the burg rules are applied, the
        internal representation is based on trees, so you get the
        left/right pointers (state->left and state->right
        respectively, the result is stored in state->reg1).

        This instruction implementation doesn't require arch-specific
        changes (it is using the MONO_EMIT_LOAD_MEMBASE_OP which is
        available on all platforms), and usually the produced code is
        fast.
        
        Next we have opcodes that must be implemented with new low-level
        architecture specific instructions (either because of performance
        considerations or because the functionality can't get implemented in
        other ways).  

        You also need a burg rule in this case, too. For example,
        consider the OP_CHECK_THIS opcode (used to raise an exception
        if the this pointer is null). The burg rule simply reads:
        
        stmt: OP_CHECK_THIS (reg) {
                mono_bblock_add_inst (s->cbb, tree);
        }
        
        Note that this opcode does not return a value (hence the
        "stmt") and it takes a register as input.

        mono_bblock_add_inst (s->cbb, tree) just adds the instruction
        (the tree variable) to the current basic block (s->cbb). In
        mini this is the place where the internal representation
        switches from the tree format to the low-level format (the
        list of simple instructions).

        In this case the actual opcode implementation is delegated to
        the arch-specific code.  A low-level opcode needs an entry in
        the machine description (the *.md files in mini/). This entry
        describes what kind of registers are used if any by the
        instruction, as well as other details such as constraints or
        other hints to the low-level engine which are architecture
        specific.  

        cpu-pentium.md, for example has the following entry:
        
        checkthis: src1:b len:3
        
        This means the instruction uses an integer register as a base
        pointer (basically a load or store is done on it) and it takes
        3 bytes of native code to implement it.

        Now you just need to provide the low-level implementation for
        the opcode in one of the mini-$arch.c files, in the
        mono_arch_output_basic_block() function. There is a big switch
        here too. The x86 implementation is:

                case OP_CHECK_THIS:
                        /* ensure ins->sreg1 is not NULL */
                        x86_alu_membase_imm (code, X86_CMP, ins->sreg1, 0, 0);
                        break;
        
        If the $arch-codegen.h header file doesn't have the code to
        emit the low-level native code, you'll need to write that as
        well.  

        Complex opcodes with register constraints may require other
        changes to the local register allocator, but usually they are
        not needed.
                
* Future

        Profile-based optimization is something that we are very
        interested in supporting.  There are two possible usage
        scenarios: 

           * Based on the profile information gathered during
             the execution of a program, hot methods can be compiled
             with the highest level of optimizations, while bootstrap
             code and cold methods can be compiled with the least set
             of optimizations and placed in a discardable list.

           * Code reordering: this profile-based optimization would
             only make sense for pre-compiled code.  The profile
             information is used to re-order the assembly code on disk
             so that the code is placed on the disk in a way that
             increments locality.  

             This is the same principle under which SGI's cord program
             works.  

        The nature of the CIL allows the above optimizations to be
        easy to implement and deploy.  Since we live and define our
        universe for these things, there are no interactions with
        system tools required, nor upgrades on the underlying
        infrastructure required.

        Instruction scheduling is important for certain kinds of
        processors, and some of the framework exists today in our
        register allocator and the instruction selector to cope with
        this, but has not been finished.  The instruction selection
        would happen at the same time as local register allocation. <