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+==============================================
+Kaleidoscope: Adding JIT and Optimizer Support
+==============================================
+
+.. contents::
+   :local:
+
+Chapter 4 Introduction
+======================
+
+Welcome to Chapter 4 of the "`Implementing a language with
+LLVM <index.html>`_" tutorial. Chapters 1-3 described the implementation
+of a simple language and added support for generating LLVM IR. This
+chapter describes two new techniques: adding optimizer support to your
+language, and adding JIT compiler support. These additions will
+demonstrate how to get nice, efficient code for the Kaleidoscope
+language.
+
+Trivial Constant Folding
+========================
+
+Our demonstration for Chapter 3 is elegant and easy to extend.
+Unfortunately, it does not produce wonderful code. The IRBuilder,
+however, does give us obvious optimizations when compiling simple code:
+
+::
+
+    ready> def test(x) 1+2+x;
+    Read function definition:
+    define double @test(double %x) {
+    entry:
+            %addtmp = fadd double 3.000000e+00, %x
+            ret double %addtmp
+    }
+
+This code is not a literal transcription of the AST built by parsing the
+input. That would be:
+
+::
+
+    ready> def test(x) 1+2+x;
+    Read function definition:
+    define double @test(double %x) {
+    entry:
+            %addtmp = fadd double 2.000000e+00, 1.000000e+00
+            %addtmp1 = fadd double %addtmp, %x
+            ret double %addtmp1
+    }
+
+Constant folding, as seen above, in particular, is a very common and
+very important optimization: so much so that many language implementors
+implement constant folding support in their AST representation.
+
+With LLVM, you don't need this support in the AST. Since all calls to
+build LLVM IR go through the LLVM IR builder, the builder itself checked
+to see if there was a constant folding opportunity when you call it. If
+so, it just does the constant fold and return the constant instead of
+creating an instruction.
+
+Well, that was easy :). In practice, we recommend always using
+``IRBuilder`` when generating code like this. It has no "syntactic
+overhead" for its use (you don't have to uglify your compiler with
+constant checks everywhere) and it can dramatically reduce the amount of
+LLVM IR that is generated in some cases (particular for languages with a
+macro preprocessor or that use a lot of constants).
+
+On the other hand, the ``IRBuilder`` is limited by the fact that it does
+all of its analysis inline with the code as it is built. If you take a
+slightly more complex example:
+
+::
+
+    ready> def test(x) (1+2+x)*(x+(1+2));
+    ready> Read function definition:
+    define double @test(double %x) {
+    entry:
+            %addtmp = fadd double 3.000000e+00, %x
+            %addtmp1 = fadd double %x, 3.000000e+00
+            %multmp = fmul double %addtmp, %addtmp1
+            ret double %multmp
+    }
+
+In this case, the LHS and RHS of the multiplication are the same value.
+We'd really like to see this generate "``tmp = x+3; result = tmp*tmp;``"
+instead of computing "``x+3``" twice.
+
+Unfortunately, no amount of local analysis will be able to detect and
+correct this. This requires two transformations: reassociation of
+expressions (to make the add's lexically identical) and Common
+Subexpression Elimination (CSE) to delete the redundant add instruction.
+Fortunately, LLVM provides a broad range of optimizations that you can
+use, in the form of "passes".
+
+LLVM Optimization Passes
+========================
+
+LLVM provides many optimization passes, which do many different sorts of
+things and have different tradeoffs. Unlike other systems, LLVM doesn't
+hold to the mistaken notion that one set of optimizations is right for
+all languages and for all situations. LLVM allows a compiler implementor
+to make complete decisions about what optimizations to use, in which
+order, and in what situation.
+
+As a concrete example, LLVM supports both "whole module" passes, which
+look across as large of body of code as they can (often a whole file,
+but if run at link time, this can be a substantial portion of the whole
+program). It also supports and includes "per-function" passes which just
+operate on a single function at a time, without looking at other
+functions. For more information on passes and how they are run, see the
+`How to Write a Pass <../WritingAnLLVMPass.html>`_ document and the
+`List of LLVM Passes <../Passes.html>`_.
+
+For Kaleidoscope, we are currently generating functions on the fly, one
+at a time, as the user types them in. We aren't shooting for the
+ultimate optimization experience in this setting, but we also want to
+catch the easy and quick stuff where possible. As such, we will choose
+to run a few per-function optimizations as the user types the function
+in. If we wanted to make a "static Kaleidoscope compiler", we would use
+exactly the code we have now, except that we would defer running the
+optimizer until the entire file has been parsed.
+
+In order to get per-function optimizations going, we need to set up a
+`FunctionPassManager <../WritingAnLLVMPass.html#passmanager>`_ to hold
+and organize the LLVM optimizations that we want to run. Once we have
+that, we can add a set of optimizations to run. The code looks like
+this:
+
+.. code-block:: c++
+
+      FunctionPassManager OurFPM(TheModule);
+
+      // Set up the optimizer pipeline.  Start with registering info about how the
+      // target lays out data structures.
+      OurFPM.add(new DataLayout(*TheExecutionEngine->getDataLayout()));
+      // Provide basic AliasAnalysis support for GVN.
+      OurFPM.add(createBasicAliasAnalysisPass());
+      // Do simple "peephole" optimizations and bit-twiddling optzns.
+      OurFPM.add(createInstructionCombiningPass());
+      // Reassociate expressions.
+      OurFPM.add(createReassociatePass());
+      // Eliminate Common SubExpressions.
+      OurFPM.add(createGVNPass());
+      // Simplify the control flow graph (deleting unreachable blocks, etc).
+      OurFPM.add(createCFGSimplificationPass());
+
+      OurFPM.doInitialization();
+
+      // Set the global so the code gen can use this.
+      TheFPM = &OurFPM;
+
+      // Run the main "interpreter loop" now.
+      MainLoop();
+
+This code defines a ``FunctionPassManager``, "``OurFPM``". It requires a
+pointer to the ``Module`` to construct itself. Once it is set up, we use
+a series of "add" calls to add a bunch of LLVM passes. The first pass is
+basically boilerplate, it adds a pass so that later optimizations know
+how the data structures in the program are laid out. The
+"``TheExecutionEngine``" variable is related to the JIT, which we will
+get to in the next section.
+
+In this case, we choose to add 4 optimization passes. The passes we
+chose here are a pretty standard set of "cleanup" optimizations that are
+useful for a wide variety of code. I won't delve into what they do but,
+believe me, they are a good starting place :).
+
+Once the PassManager is set up, we need to make use of it. We do this by
+running it after our newly created function is constructed (in
+``FunctionAST::Codegen``), but before it is returned to the client:
+
+.. code-block:: c++
+
+      if (Value *RetVal = Body->Codegen()) {
+        // Finish off the function.
+        Builder.CreateRet(RetVal);
+
+        // Validate the generated code, checking for consistency.
+        verifyFunction(*TheFunction);
+
+        // Optimize the function.
+        TheFPM->run(*TheFunction);
+
+        return TheFunction;
+      }
+
+As you can see, this is pretty straightforward. The
+``FunctionPassManager`` optimizes and updates the LLVM Function\* in
+place, improving (hopefully) its body. With this in place, we can try
+our test above again:
+
+::
+
+    ready> def test(x) (1+2+x)*(x+(1+2));
+    ready> Read function definition:
+    define double @test(double %x) {
+    entry:
+            %addtmp = fadd double %x, 3.000000e+00
+            %multmp = fmul double %addtmp, %addtmp
+            ret double %multmp
+    }
+
+As expected, we now get our nicely optimized code, saving a floating
+point add instruction from every execution of this function.
+
+LLVM provides a wide variety of optimizations that can be used in
+certain circumstances. Some `documentation about the various
+passes <../Passes.html>`_ is available, but it isn't very complete.
+Another good source of ideas can come from looking at the passes that
+``Clang`` runs to get started. The "``opt``" tool allows you to
+experiment with passes from the command line, so you can see if they do
+anything.
+
+Now that we have reasonable code coming out of our front-end, lets talk
+about executing it!
+
+Adding a JIT Compiler
+=====================
+
+Code that is available in LLVM IR can have a wide variety of tools
+applied to it. For example, you can run optimizations on it (as we did
+above), you can dump it out in textual or binary forms, you can compile
+the code to an assembly file (.s) for some target, or you can JIT
+compile it. The nice thing about the LLVM IR representation is that it
+is the "common currency" between many different parts of the compiler.
+
+In this section, we'll add JIT compiler support to our interpreter. The
+basic idea that we want for Kaleidoscope is to have the user enter
+function bodies as they do now, but immediately evaluate the top-level
+expressions they type in. For example, if they type in "1 + 2;", we
+should evaluate and print out 3. If they define a function, they should
+be able to call it from the command line.
+
+In order to do this, we first declare and initialize the JIT. This is
+done by adding a global variable and a call in ``main``:
+
+.. code-block:: c++
+
+    static ExecutionEngine *TheExecutionEngine;
+    ...
+    int main() {
+      ..
+      // Create the JIT.  This takes ownership of the module.
+      TheExecutionEngine = EngineBuilder(TheModule).create();
+      ..
+    }
+
+This creates an abstract "Execution Engine" which can be either a JIT
+compiler or the LLVM interpreter. LLVM will automatically pick a JIT
+compiler for you if one is available for your platform, otherwise it
+will fall back to the interpreter.
+
+Once the ``ExecutionEngine`` is created, the JIT is ready to be used.
+There are a variety of APIs that are useful, but the simplest one is the
+"``getPointerToFunction(F)``" method. This method JIT compiles the
+specified LLVM Function and returns a function pointer to the generated
+machine code. In our case, this means that we can change the code that
+parses a top-level expression to look like this:
+
+.. code-block:: c++
+
+    static void HandleTopLevelExpression() {
+      // Evaluate a top-level expression into an anonymous function.
+      if (FunctionAST *F = ParseTopLevelExpr()) {
+        if (Function *LF = F->Codegen()) {
+          LF->dump();  // Dump the function for exposition purposes.
+
+          // JIT the function, returning a function pointer.
+          void *FPtr = TheExecutionEngine->getPointerToFunction(LF);
+
+          // Cast it to the right type (takes no arguments, returns a double) so we
+          // can call it as a native function.
+          double (*FP)() = (double (*)())(intptr_t)FPtr;
+          fprintf(stderr, "Evaluated to %f\n", FP());
+        }
+
+Recall that we compile top-level expressions into a self-contained LLVM
+function that takes no arguments and returns the computed double.
+Because the LLVM JIT compiler matches the native platform ABI, this
+means that you can just cast the result pointer to a function pointer of
+that type and call it directly. This means, there is no difference
+between JIT compiled code and native machine code that is statically
+linked into your application.
+
+With just these two changes, lets see how Kaleidoscope works now!
+
+::
+
+    ready> 4+5;
+    Read top-level expression:
+    define double @0() {
+    entry:
+      ret double 9.000000e+00
+    }
+
+    Evaluated to 9.000000
+
+Well this looks like it is basically working. The dump of the function
+shows the "no argument function that always returns double" that we
+synthesize for each top-level expression that is typed in. This
+demonstrates very basic functionality, but can we do more?
+
+::
+
+    ready> def testfunc(x y) x + y*2;
+    Read function definition:
+    define double @testfunc(double %x, double %y) {
+    entry:
+      %multmp = fmul double %y, 2.000000e+00
+      %addtmp = fadd double %multmp, %x
+      ret double %addtmp
+    }
+
+    ready> testfunc(4, 10);
+    Read top-level expression:
+    define double @1() {
+    entry:
+      %calltmp = call double @testfunc(double 4.000000e+00, double 1.000000e+01)
+      ret double %calltmp
+    }
+
+    Evaluated to 24.000000
+
+This illustrates that we can now call user code, but there is something
+a bit subtle going on here. Note that we only invoke the JIT on the
+anonymous functions that *call testfunc*, but we never invoked it on
+*testfunc* itself. What actually happened here is that the JIT scanned
+for all non-JIT'd functions transitively called from the anonymous
+function and compiled all of them before returning from
+``getPointerToFunction()``.
+
+The JIT provides a number of other more advanced interfaces for things
+like freeing allocated machine code, rejit'ing functions to update them,
+etc. However, even with this simple code, we get some surprisingly
+powerful capabilities - check this out (I removed the dump of the
+anonymous functions, you should get the idea by now :) :
+
+::
+
+    ready> extern sin(x);
+    Read extern:
+    declare double @sin(double)
+
+    ready> extern cos(x);
+    Read extern:
+    declare double @cos(double)
+
+    ready> sin(1.0);
+    Read top-level expression:
+    define double @2() {
+    entry:
+      ret double 0x3FEAED548F090CEE
+    }
+
+    Evaluated to 0.841471
+
+    ready> def foo(x) sin(x)*sin(x) + cos(x)*cos(x);
+    Read function definition:
+    define double @foo(double %x) {
+    entry:
+      %calltmp = call double @sin(double %x)
+      %multmp = fmul double %calltmp, %calltmp
+      %calltmp2 = call double @cos(double %x)
+      %multmp4 = fmul double %calltmp2, %calltmp2
+      %addtmp = fadd double %multmp, %multmp4
+      ret double %addtmp
+    }
+
+    ready> foo(4.0);
+    Read top-level expression:
+    define double @3() {
+    entry:
+      %calltmp = call double @foo(double 4.000000e+00)
+      ret double %calltmp
+    }
+
+    Evaluated to 1.000000
+
+Whoa, how does the JIT know about sin and cos? The answer is
+surprisingly simple: in this example, the JIT started execution of a
+function and got to a function call. It realized that the function was
+not yet JIT compiled and invoked the standard set of routines to resolve
+the function. In this case, there is no body defined for the function,
+so the JIT ended up calling "``dlsym("sin")``" on the Kaleidoscope
+process itself. Since "``sin``" is defined within the JIT's address
+space, it simply patches up calls in the module to call the libm version
+of ``sin`` directly.
+
+The LLVM JIT provides a number of interfaces (look in the
+``ExecutionEngine.h`` file) for controlling how unknown functions get
+resolved. It allows you to establish explicit mappings between IR
+objects and addresses (useful for LLVM global variables that you want to
+map to static tables, for example), allows you to dynamically decide on
+the fly based on the function name, and even allows you to have the JIT
+compile functions lazily the first time they're called.
+
+One interesting application of this is that we can now extend the
+language by writing arbitrary C++ code to implement operations. For
+example, if we add:
+
+.. code-block:: c++
+
+    /// putchard - putchar that takes a double and returns 0.
+    extern "C"
+    double putchard(double X) {
+      putchar((char)X);
+      return 0;
+    }
+
+Now we can produce simple output to the console by using things like:
+"``extern putchard(x); putchard(120);``", which prints a lowercase 'x'
+on the console (120 is the ASCII code for 'x'). Similar code could be
+used to implement file I/O, console input, and many other capabilities
+in Kaleidoscope.
+
+This completes the JIT and optimizer chapter of the Kaleidoscope
+tutorial. At this point, we can compile a non-Turing-complete
+programming language, optimize and JIT compile it in a user-driven way.
+Next up we'll look into `extending the language with control flow
+constructs <LangImpl5.html>`_, tackling some interesting LLVM IR issues
+along the way.
+
+Full Code Listing
+=================
+
+Here is the complete code listing for our running example, enhanced with
+the LLVM JIT and optimizer. To build this example, use:
+
+.. code-block:: bash
+
+    # Compile
+    clang++ -g toy.cpp `llvm-config --cppflags --ldflags --libs core jit native` -O3 -o toy
+    # Run
+    ./toy
+
+If you are compiling this on Linux, make sure to add the "-rdynamic"
+option as well. This makes sure that the external functions are resolved
+properly at runtime.
+
+Here is the code:
+
+.. literalinclude:: ../../examples/Kaleidoscope/Chapter4/toy.cpp
+   :language: c++
+
+`Next: Extending the language: control flow <LangImpl5.html>`_
+