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Building a JIT: Starting out with KaleidoscopeJIT
.. contents::
Chapter 1 Introduction
Welcome to Chapter 1 of the "Building an ORC-based JIT in LLVM" tutorial. This
tutorial runs through the implementation of a JIT compiler using LLVM's
On-Request-Compilation (ORC) APIs. It begins with a simplified version of the
KaleidoscopeJIT class used in the
`Implementing a language with LLVM <LangImpl1.html>`_ tutorials and then
introduces new features like optimization, lazy compilation and remote
The goal of this tutorial is to introduce you to LLVM's ORC JIT APIs, show how
these APIs interact with other parts of LLVM, and to teach you how to recombine
them to build a custom JIT that is suited to your use-case.
The structure of the tutorial is:
- Chapter #1: Investigate the simple KaleidoscopeJIT class. This will
introduce some of the basic concepts of the ORC JIT APIs, including the
idea of an ORC *Layer*.
- `Chapter #2 <BuildingAJIT2.html>`_: Extend the basic KaleidoscopeJIT by adding
a new layer that will optimize IR and generated code.
- `Chapter #3 <BuildingAJIT3.html>`_: Further extend the JIT by adding a
Compile-On-Demand layer to lazily compile IR.
- `Chapter #4 <BuildingAJIT4.html>`_: Improve the laziness of our JIT by
replacing the Compile-On-Demand layer with a custom layer that uses the ORC
Compile Callbacks API directly to defer IR-generation until functions are
- `Chapter #5 <BuildingAJIT5.html>`_: Add process isolation by JITing code into
a remote process with reduced privileges using the JIT Remote APIs.
To provide input for our JIT we will use the Kaleidoscope REPL from
`Chapter 7 <LangImpl7.html>`_ of the "Implementing a language in LLVM tutorial",
with one minor modification: We will remove the FunctionPassManager from the
code for that chapter and replace it with optimization support in our JIT class
in Chapter #2.
Finally, a word on API generations: ORC is the 3rd generation of LLVM JIT API.
It was preceded by MCJIT, and before that by the (now deleted) legacy JIT.
These tutorials don't assume any experience with these earlier APIs, but
readers acquainted with them will see many familiar elements. Where appropriate
we will make this connection with the earlier APIs explicit to help people who
are transitioning from them to ORC.
JIT API Basics
The purpose of a JIT compiler is to compile code "on-the-fly" as it is needed,
rather than compiling whole programs to disk ahead of time as a traditional
compiler does. To support that aim our initial, bare-bones JIT API will be:
1. Handle addModule(Module &M) -- Make the given IR module available for
2. JITSymbol findSymbol(const std::string &Name) -- Search for pointers to
symbols (functions or variables) that have been added to the JIT.
3. void removeModule(Handle H) -- Remove a module from the JIT, releasing any
memory that had been used for the compiled code.
A basic use-case for this API, executing the 'main' function from a module,
will look like:
.. code-block:: c++
std::unique_ptr<Module> M = buildModule();
Handle H = J.addModule(*M);
int (*Main)(int, char*[]) =
(int(*)(int, char*[])J.findSymbol("main").getAddress();
int Result = Main();
The APIs that we build in these tutorials will all be variations on this simple
theme. Behind the API we will refine the implementation of the JIT to add
support for optimization and lazy compilation. Eventually we will extend the
API itself to allow higher-level program representations (e.g. ASTs) to be
added to the JIT.
In the previous section we described our API, now we examine a simple
implementation of it: The KaleidoscopeJIT class [1]_ that was used in the
`Implementing a language with LLVM <LangImpl1.html>`_ tutorials. We will use
the REPL code from `Chapter 7 <LangImpl7.html>`_ of that tutorial to supply the
input for our JIT: Each time the user enters an expression the REPL will add a
new IR module containing the code for that expression to the JIT. If the
expression is a top-level expression like '1+1' or 'sin(x)', the REPL will also
use the findSymbol method of our JIT class find and execute the code for the
expression, and then use the removeModule method to remove the code again
(since there's no way to re-invoke an anonymous expression). In later chapters
of this tutorial we'll modify the REPL to enable new interactions with our JIT
class, but for now we will take this setup for granted and focus our attention on
the implementation of our JIT itself.
Our KaleidoscopeJIT class is defined in the KaleidoscopeJIT.h header. After the
usual include guards and #includes [2]_, we get to the definition of our class:
.. code-block:: c++
#include "llvm/ExecutionEngine/ExecutionEngine.h"
#include "llvm/ExecutionEngine/RTDyldMemoryManager.h"
#include "llvm/ExecutionEngine/Orc/CompileUtils.h"
#include "llvm/ExecutionEngine/Orc/IRCompileLayer.h"
#include "llvm/ExecutionEngine/Orc/LambdaResolver.h"
#include "llvm/ExecutionEngine/Orc/ObjectLinkingLayer.h"
#include "llvm/IR/Mangler.h"
#include "llvm/Support/DynamicLibrary.h"
namespace llvm {
namespace orc {
class KaleidoscopeJIT {
std::unique_ptr<TargetMachine> TM;
const DataLayout DL;
ObjectLinkingLayer<> ObjectLayer;
IRCompileLayer<decltype(ObjectLayer)> CompileLayer;
typedef decltype(CompileLayer)::ModuleSetHandleT ModuleHandleT;
Our class begins with four members: A TargetMachine, TM, which will be used
to build our LLVM compiler instance; A DataLayout, DL, which will be used for
symbol mangling (more on that later), and two ORC *layers*: an
ObjectLinkingLayer and a IRCompileLayer. We'll be talking more about layers in
the next chapter, but for now you can think of them as analogous to LLVM
Passes: they wrap up useful JIT utilities behind an easy to compose interface.
The first layer, ObjectLinkingLayer, is the foundation of our JIT: it takes
in-memory object files produced by a compiler and links them on the fly to make
them executable. This JIT-on-top-of-a-linker design was introduced in MCJIT,
however the linker was hidden inside the MCJIT class. In ORC we expose the
linker so that clients can access and configure it directly if they need to. In
this tutorial our ObjectLinkingLayer will just be used to support the next layer
in our stack: the IRCompileLayer, which will be responsible for taking LLVM IR,
compiling it, and passing the resulting in-memory object files down to the
object linking layer below.
That's it for member variables, after that we have a single typedef:
ModuleHandleT. This is the handle type that will be returned from our JIT's
addModule method, and can be passed to the removeModule method to remove a
module. The IRCompileLayer class already provides a convenient handle type
(IRCompileLayer::ModuleSetHandleT), so we just alias our ModuleHandleT to this.
.. code-block:: c++
: TM(EngineBuilder().selectTarget()), DL(TM->createDataLayout()),
CompileLayer(ObjectLayer, SimpleCompiler(*TM)) {
TargetMachine &getTargetMachine() { return *TM; }
Next up we have our class constructor. We begin by initializing TM using the
EngineBuilder::selectTarget helper method, which constructs a TargetMachine for
the current process. Next we use our newly created TargetMachine to initialize
DL, our DataLayout. Then we initialize our IRCompileLayer. Our IRCompile layer
needs two things: (1) A reference to our object linking layer, and (2) a
compiler instance to use to perform the actual compilation from IR to object
files. We use the off-the-shelf SimpleCompiler instance for now. Finally, in
the body of the constructor, we call the DynamicLibrary::LoadLibraryPermanently
method with a nullptr argument. Normally the LoadLibraryPermanently method is
called with the path of a dynamic library to load, but when passed a null
pointer it will 'load' the host process itself, making its exported symbols
available for execution.
.. code-block:: c++
ModuleHandle addModule(std::unique_ptr<Module> M) {
// Build our symbol resolver:
// Lambda 1: Look back into the JIT itself to find symbols that are part of
// the same "logical dylib".
// Lambda 2: Search for external symbols in the host process.
auto Resolver = createLambdaResolver(
[&](const std::string &Name) {
if (auto Sym = CompileLayer.findSymbol(Name, false))
return Sym;
return JITSymbol(nullptr);
[](const std::string &S) {
if (auto SymAddr =
return JITSymbol(SymAddr, JITSymbolFlags::Exported);
return JITSymbol(nullptr);
// Build a singleton module set to hold our module.
std::vector<std::unique_ptr<Module>> Ms;
// Add the set to the JIT with the resolver we created above and a newly
// created SectionMemoryManager.
return CompileLayer.addModuleSet(std::move(Ms),
Now we come to the first of our JIT API methods: addModule. This method is
responsible for adding IR to the JIT and making it available for execution. In
this initial implementation of our JIT we will make our modules "available for
execution" by adding them straight to the IRCompileLayer, which will
immediately compile them. In later chapters we will teach our JIT to be lazier
and instead add the Modules to a "pending" list to be compiled if and when they
are first executed.
To add our module to the IRCompileLayer we need to supply two auxiliary objects
(as well as the module itself): a memory manager and a symbol resolver. The
memory manager will be responsible for managing the memory allocated to JIT'd
machine code, setting memory permissions, and registering exception handling
tables (if the JIT'd code uses exceptions). For our memory manager we will use
the SectionMemoryManager class: another off-the-shelf utility that provides all
the basic functionality we need. The second auxiliary class, the symbol
resolver, is more interesting for us. It exists to tell the JIT where to look
when it encounters an *external symbol* in the module we are adding. External
symbols are any symbol not defined within the module itself, including calls to
functions outside the JIT and calls to functions defined in other modules that
have already been added to the JIT. It may seem as though modules added to the
JIT should "know about one another" by default, but since we would still have to
supply a symbol resolver for references to code outside the JIT it turns out to
be easier to just re-use this one mechanism for all symbol resolution. This has
the added benefit that the user has full control over the symbol resolution
process. Should we search for definitions within the JIT first, then fall back
on external definitions? Or should we prefer external definitions where
available and only JIT code if we don't already have an available
implementation? By using a single symbol resolution scheme we are free to choose
whatever makes the most sense for any given use case.
Building a symbol resolver is made especially easy by the *createLambdaResolver*
function. This function takes two lambdas [3]_ and returns a JITSymbolResolver
instance. The first lambda is used as the implementation of the resolver's
findSymbolInLogicalDylib method, which searches for symbol definitions that
should be thought of as being part of the same "logical" dynamic library as this
Module. If you are familiar with static linking: this means that
findSymbolInLogicalDylib should expose symbols with common linkage and hidden
visibility. If all this sounds foreign you can ignore the details and just
remember that this is the first method that the linker will use to try to find a
symbol definition. If the findSymbolInLogicalDylib method returns a null result
then the linker will call the second symbol resolver method, called findSymbol,
which searches for symbols that should be thought of as external to (but
visibile from) the module and its logical dylib. In this tutorial we will adopt
the following simple scheme: All modules added to the JIT will behave as if they
were linked into a single, ever-growing logical dylib. To implement this our
first lambda (the one defining findSymbolInLogicalDylib) will just search for
JIT'd code by calling the CompileLayer's findSymbol method. If we don't find a
symbol in the JIT itself we'll fall back to our second lambda, which implements
findSymbol. This will use the RTDyldMemoryManager::getSymbolAddressInProcess
method to search for the symbol within the program itself. If we can't find a
symbol definition via either of these paths, the JIT will refuse to accept our
module, returning a "symbol not found" error.
Now that we've built our symbol resolver, we're ready to add our module to the
JIT. We do this by calling the CompileLayer's addModuleSet method [4]_. Since
we only have a single Module and addModuleSet expects a collection, we will
create a vector of modules and add our module as the only member. Since we
have already typedef'd our ModuleHandleT type to be the same as the
CompileLayer's handle type, we can return the handle from addModuleSet
directly from our addModule method.
.. code-block:: c++
JITSymbol findSymbol(const std::string Name) {
std::string MangledName;
raw_string_ostream MangledNameStream(MangledName);
Mangler::getNameWithPrefix(MangledNameStream, Name, DL);
return CompileLayer.findSymbol(MangledNameStream.str(), true);
void removeModule(ModuleHandle H) {
Now that we can add code to our JIT, we need a way to find the symbols we've
added to it. To do that we call the findSymbol method on our IRCompileLayer,
but with a twist: We have to *mangle* the name of the symbol we're searching
for first. The reason for this is that the ORC JIT components use mangled
symbols internally the same way a static compiler and linker would, rather
than using plain IR symbol names. The kind of mangling will depend on the
DataLayout, which in turn depends on the target platform. To allow us to
remain portable and search based on the un-mangled name, we just re-produce
this mangling ourselves.
We now come to the last method in our JIT API: removeModule. This method is
responsible for destructing the MemoryManager and SymbolResolver that were
added with a given module, freeing any resources they were using in the
process. In our Kaleidoscope demo we rely on this method to remove the module
representing the most recent top-level expression, preventing it from being
treated as a duplicate definition when the next top-level expression is
entered. It is generally good to free any module that you know you won't need
to call further, just to free up the resources dedicated to it. However, you
don't strictly need to do this: All resources will be cleaned up when your
JIT class is destructed, if they haven't been freed before then.
This brings us to the end of Chapter 1 of Building a JIT. You now have a basic
but fully functioning JIT stack that you can use to take LLVM IR and make it
executable within the context of your JIT process. In the next chapter we'll
look at how to extend this JIT to produce better quality code, and in the
process take a deeper look at the ORC layer concept.
`Next: Extending the KaleidoscopeJIT <BuildingAJIT2.html>`_
Full Code Listing
Here is the complete code listing for our running example. To build this
example, use:
.. code-block:: bash
# Compile
clang++ -g toy.cpp `llvm-config --cxxflags --ldflags --system-libs --libs core orc native` -O3 -o toy
# Run
Here is the code:
.. literalinclude:: ../../examples/Kaleidoscope/BuildingAJIT/Chapter1/KaleidoscopeJIT.h
:language: c++
.. [1] Actually we use a cut-down version of KaleidoscopeJIT that makes a
simplifying assumption: symbols cannot be re-defined. This will make it
impossible to re-define symbols in the REPL, but will make our symbol
lookup logic simpler. Re-introducing support for symbol redefinition is
left as an exercise for the reader. (The KaleidoscopeJIT.h used in the
original tutorials will be a helpful reference).
.. [2] +-----------------------+-----------------------------------------------+
| File | Reason for inclusion |
| ExecutionEngine.h | Access to the EngineBuilder::selectTarget |
| | method. |
| | Access to the |
| RTDyldMemoryManager.h | RTDyldMemoryManager::getSymbolAddressInProcess|
| | method. |
| CompileUtils.h | Provides the SimpleCompiler class. |
| IRCompileLayer.h | Provides the IRCompileLayer class. |
| | Access the createLambdaResolver function, |
| LambdaResolver.h | which provides easy construction of symbol |
| | resolvers. |
| ObjectLinkingLayer.h | Provides the ObjectLinkingLayer class. |
| Mangler.h | Provides the Mangler class for platform |
| | specific name-mangling. |
| DynamicLibrary.h | Provides the DynamicLibrary class, which |
| | makes symbols in the host process searchable. |
.. [3] Actually they don't have to be lambdas, any object with a call operator
will do, including plain old functions or std::functions.
.. [4] ORC layers accept sets of Modules, rather than individual ones, so that
all Modules in the set could be co-located by the memory manager, though
this feature is not yet implemented.