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ORC Design and Implementation
.. contents::
This document aims to provide a high-level overview of the design and
implementation of the ORC JIT APIs. Except where otherwise stated all discussion
refers to the modern ORCv2 APIs (available since LLVM 7). Clients wishing to
transition from OrcV1 should see Section :ref:`transitioning_orcv1_to_orcv2`.
ORC provides a modular API for building JIT compilers. There are a number
of use cases for such an API. For example:
1. The LLVM tutorials use a simple ORC-based JIT class to execute expressions
compiled from a toy language: Kaleidoscope.
2. The LLVM debugger, LLDB, uses a cross-compiling JIT for expression
evaluation. In this use case, cross compilation allows expressions compiled
in the debugger process to be executed on the debug target process, which may
be on a different device/architecture.
3. In high-performance JITs (e.g. JVMs, Julia) that want to make use of LLVM's
optimizations within an existing JIT infrastructure.
4. In interpreters and REPLs, e.g. Cling (C++) and the Swift interpreter.
By adopting a modular, library-based design we aim to make ORC useful in as many
of these contexts as possible.
ORC provides the following features:
ORC provides APIs to link relocatable object files (COFF, ELF, MachO) [1]_
into a target process at runtime. The target process may be the same process
that contains the JIT session object and jit-linker, or may be another process
(even one running on a different machine or architecture) that communicates
with the JIT via RPC.
**LLVM IR compilation**
ORC provides off the shelf components (IRCompileLayer, SimpleCompiler,
ConcurrentIRCompiler) that make it easy to add LLVM IR to a JIT'd process.
**Eager and lazy compilation**
By default, ORC will compile symbols as soon as they are looked up in the JIT
session object (``ExecutionSession``). Compiling eagerly by default makes it
easy to use ORC as an in-memory compiler for an existing JIT (similar to how
MCJIT is commonly used). However ORC also provides built-in support for lazy
compilation via lazy-reexports (see :ref:`Laziness`).
**Support for Custom Compilers and Program Representations**
Clients can supply custom compilers for each symbol that they define in their
JIT session. ORC will run the user-supplied compiler when the a definition of
a symbol is needed. ORC is actually fully language agnostic: LLVM IR is not
treated specially, and is supported via the same wrapper mechanism (the
``MaterializationUnit`` class) that is used for custom compilers.
**Concurrent JIT'd code** and **Concurrent Compilation**
JIT'd code may be executed in multiple threads, may spawn new threads, and may
re-enter the ORC (e.g. to request lazy compilation) concurrently from multiple
threads. Compilers launched my ORC can run concurrently (provided the client
sets up an appropriate dispatcher). Built-in dependency tracking ensures that
ORC does not release pointers to JIT'd code or data until all dependencies
have also been JIT'd and they are safe to call or use.
**Removable Code**
Resources for JIT'd program representations
**Orthogonality** and **Composability**
Each of the features above can be used independently. It is possible to put
ORC components together to make a non-lazy, in-process, single threaded JIT
or a lazy, out-of-process, concurrent JIT, or anything in between.
ORC provides two basic JIT classes off-the-shelf. These are useful both as
examples of how to assemble ORC components to make a JIT, and as replacements
for earlier LLVM JIT APIs (e.g. MCJIT).
The LLJIT class uses an IRCompileLayer and RTDyldObjectLinkingLayer to support
compilation of LLVM IR and linking of relocatable object files. All operations
are performed eagerly on symbol lookup (i.e. a symbol's definition is compiled
as soon as you attempt to look up its address). LLJIT is a suitable replacement
for MCJIT in most cases (note: some more advanced features, e.g.
JITEventListeners are not supported yet).
The LLLazyJIT extends LLJIT and adds a CompileOnDemandLayer to enable lazy
compilation of LLVM IR. When an LLVM IR module is added via the addLazyIRModule
method, function bodies in that module will not be compiled until they are first
called. LLLazyJIT aims to provide a replacement of LLVM's original (pre-MCJIT)
LLJIT and LLLazyJIT instances can be created using their respective builder
classes: LLJITBuilder and LLazyJITBuilder. For example, assuming you have a
module ``M`` loaded on a ThreadSafeContext ``Ctx``:
.. code-block:: c++
// Try to detect the host arch and construct an LLJIT instance.
auto JIT = LLJITBuilder().create();
// If we could not construct an instance, return an error.
if (!JIT)
return JIT.takeError();
// Add the module.
if (auto Err = JIT->addIRModule(TheadSafeModule(std::move(M), Ctx)))
return Err;
// Look up the JIT'd code entry point.
auto EntrySym = JIT->lookup("entry");
if (!EntrySym)
return EntrySym.takeError();
// Cast the entry point address to a function pointer.
auto *Entry = (void(*)())EntrySym.getAddress();
// Call into JIT'd code.
The builder classes provide a number of configuration options that can be
specified before the JIT instance is constructed. For example:
.. code-block:: c++
// Build an LLLazyJIT instance that uses four worker threads for compilation,
// and jumps to a specific error handler (rather than null) on lazy compile
// failures.
void handleLazyCompileFailure() {
// JIT'd code will jump here if lazy compilation fails, giving us an
// opportunity to exit or throw an exception into JIT'd code.
throw JITFailed();
auto JIT = LLLazyJITBuilder()
// ...
For users wanting to get started with LLJIT a minimal example program can be
found at ``llvm/examples/HowToUseLLJIT``.
Design Overview
ORC's JIT program model aims to emulate the linking and symbol resolution
rules used by the static and dynamic linkers. This allows ORC to JIT
arbitrary LLVM IR, including IR produced by an ordinary static compiler (e.g.
clang) that uses constructs like symbol linkage and visibility, and weak [3]_
and common symbol definitions.
To see how this works, imagine a program ``foo`` which links against a pair
of dynamic libraries: ``libA`` and ``libB``. On the command line, building this
program might look like:
.. code-block:: bash
$ clang++ -shared -o libA.dylib a1.cpp a2.cpp
$ clang++ -shared -o libB.dylib b1.cpp b2.cpp
$ clang++ -o myapp myapp.cpp -L. -lA -lB
$ ./myapp
In ORC, this would translate into API calls on a hypothetical CXXCompilingLayer
(with error checking omitted for brevity) as:
.. code-block:: c++
ExecutionSession ES;
RTDyldObjectLinkingLayer ObjLinkingLayer(
ES, []() { return std::make_unique<SectionMemoryManager>(); });
CXXCompileLayer CXXLayer(ES, ObjLinkingLayer);
// Create JITDylib "A" and add code to it using the CXX layer.
auto &LibA = ES.createJITDylib("A");
CXXLayer.add(LibA, MemoryBuffer::getFile("a1.cpp"));
CXXLayer.add(LibA, MemoryBuffer::getFile("a2.cpp"));
// Create JITDylib "B" and add code to it using the CXX layer.
auto &LibB = ES.createJITDylib("B");
CXXLayer.add(LibB, MemoryBuffer::getFile("b1.cpp"));
CXXLayer.add(LibB, MemoryBuffer::getFile("b2.cpp"));
// Create and specify the search order for the main JITDylib. This is
// equivalent to a "links against" relationship in a command-line link.
auto &MainJD = ES.createJITDylib("main");
CXXLayer.add(MainJD, MemoryBuffer::getFile("main.cpp"));
// Look up the JIT'd main, cast it to a function pointer, then call it.
auto MainSym = ExitOnErr(ES.lookup({&MainJD}, "main"));
auto *Main = (int(*)(int, char*[]))MainSym.getAddress();
int Result = Main(...);
This example tells us nothing about *how* or *when* compilation will happen.
That will depend on the implementation of the hypothetical CXXCompilingLayer.
The same linker-based symbol resolution rules will apply regardless of that
implementation, however. For example, if a1.cpp and a2.cpp both define a
function "foo" then ORCv2 will generate a duplicate definition error. On the
other hand, if a1.cpp and b1.cpp both define "foo" there is no error (different
dynamic libraries may define the same symbol). If main.cpp refers to "foo", it
should bind to the definition in LibA rather than the one in LibB, since
main.cpp is part of the "main" dylib, and the main dylib links against LibA
before LibB.
Many JIT clients will have no need for this strict adherence to the usual
ahead-of-time linking rules, and should be able to get by just fine by putting
all of their code in a single JITDylib. However, clients who want to JIT code
for languages/projects that traditionally rely on ahead-of-time linking (e.g.
C++) will find that this feature makes life much easier.
Symbol lookup in ORC serves two other important functions, beyond providing
addresses for symbols: (1) It triggers compilation of the symbol(s) searched for
(if they have not been compiled already), and (2) it provides the
synchronization mechanism for concurrent compilation. The pseudo-code for the
lookup process is:
.. code-block:: none
construct a query object from a query set and query handler
lock the session
lodge query against requested symbols, collect required materializers (if any)
unlock the session
dispatch materializers (if any)
In this context a materializer is something that provides a working definition
of a symbol upon request. Usually materializers are just wrappers for compilers,
but they may also wrap a jit-linker directly (if the program representation
backing the definitions is an object file), or may even be a class that writes
bits directly into memory (for example, if the definitions are
stubs). Materialization is the blanket term for any actions (compiling, linking,
splatting bits, registering with runtimes, etc.) that are required to generate a
symbol definition that is safe to call or access.
As each materializer completes its work it notifies the JITDylib, which in turn
notifies any query objects that are waiting on the newly materialized
definitions. Each query object maintains a count of the number of symbols that
it is still waiting on, and once this count reaches zero the query object calls
the query handler with a *SymbolMap* (a map of symbol names to addresses)
describing the result. If any symbol fails to materialize the query immediately
calls the query handler with an error.
The collected materialization units are sent to the ExecutionSession to be
dispatched, and the dispatch behavior can be set by the client. By default each
materializer is run on the calling thread. Clients are free to create new
threads to run materializers, or to send the work to a work queue for a thread
pool (this is what LLJIT/LLLazyJIT do).
Top Level APIs
Many of ORC's top-level APIs are visible in the example above:
- *ExecutionSession* represents the JIT'd program and provides context for the
JIT: It contains the JITDylibs, error reporting mechanisms, and dispatches the
- *JITDylibs* provide the symbol tables.
- *Layers* (ObjLinkingLayer and CXXLayer) are wrappers around compilers and
allow clients to add uncompiled program representations supported by those
compilers to JITDylibs.
Several other important APIs are used explicitly. JIT clients need not be aware
of them, but Layer authors will use them:
- *MaterializationUnit* - When XXXLayer::add is invoked it wraps the given
program representation (in this example, C++ source) in a MaterializationUnit,
which is then stored in the JITDylib. MaterializationUnits are responsible for
describing the definitions they provide, and for unwrapping the program
representation and passing it back to the layer when compilation is required
(this ownership shuffle makes writing thread-safe layers easier, since the
ownership of the program representation will be passed back on the stack,
rather than having to be fished out of a Layer member, which would require
- *MaterializationResponsibility* - When a MaterializationUnit hands a program
representation back to the layer it comes with an associated
MaterializationResponsibility object. This object tracks the definitions
that must be materialized and provides a way to notify the JITDylib once they
are either successfully materialized or a failure occurs.
Absolute Symbols, Aliases, and Reexports
ORC makes it easy to define symbols with absolute addresses, or symbols that
are simply aliases of other symbols:
Absolute Symbols
Absolute symbols are symbols that map directly to addresses without requiring
further materialization, for example: "foo" = 0x1234. One use case for
absolute symbols is allowing resolution of process symbols. E.g.
.. code-block: c++
{ Mangle("printf"),
{ pointerToJITTargetAddress(&printf),
JITSymbolFlags::Callable } }
With this mapping established code added to the JIT can refer to printf
symbolically rather than requiring the address of printf to be "baked in".
This in turn allows cached versions of the JIT'd code (e.g. compiled objects)
to be re-used across JIT sessions as the JIT'd code no longer changes, only the
absolute symbol definition does.
For process and library symbols the DynamicLibrarySearchGenerator utility (See
:ref:`How to Add Process and Library Symbols to JITDylibs
<ProcessAndLibrarySymbols>`) can be used to automatically build absolute
symbol mappings for you. However the absoluteSymbols function is still useful
for making non-global objects in your JIT visible to JIT'd code. For example,
imagine that your JIT standard library needs access to your JIT object to make
some calls. We could bake the address of your object into the library, but then
it would need to be recompiled for each session:
.. code-block: c++
// From standard library for JIT'd code:
class MyJIT {
void log(const char *Msg);
void log(const char *Msg) { ((MyJIT*)0x1234)->log(Msg); }
We can turn this into a symbolic reference in the JIT standard library:
.. code-block: c++
extern MyJIT *__MyJITInstance;
void log(const char *Msg) { __MyJITInstance->log(Msg); }
And then make our JIT object visible to the JIT standard library with an
absolute symbol definition when the JIT is started:
.. code-block: c++
MyJIT J = ...;
auto &JITStdLibJD = ... ;
{ Mangle("__MyJITInstance"),
{ pointerToJITTargetAddress(&J), JITSymbolFlags() } }
Aliases and Reexports
Aliases and reexports allow you to define new symbols that map to existing
symbols. This can be useful for changing linkage relationships between symbols
across sessions without having to recompile code. For example, imagine that
JIT'd code has access to a log function, ``void log(const char*)`` for which
there are two implementations in the JIT standard library: ``log_fast`` and
``log_detailed``. Your JIT can choose which one of these definitions will be
used when the ``log`` symbol is referenced by setting up an alias at JIT startup
.. code-block: c++
auto &JITStdLibJD = ... ;
auto LogImplementationSymbol =
Verbose ? Mangle("log_detailed") : Mangle("log_fast");
{ Mangle("log"),
{ LogImplementationSymbol
JITSymbolFlags::Exported | JITSymbolFlags::Callable } }
The ``symbolAliases`` function allows you to define aliases within a single
JITDylib. The ``reexports`` function provides the same functionality, but
operates across JITDylib boundaries. E.g.
.. code-block: c++
auto &JD1 = ... ;
auto &JD2 = ... ;
// Make 'bar' in JD2 an alias for 'foo' from JD1.
reexports(JD1, SymbolAliasMap({
{ Mangle("bar"), { Mangle("foo"), JITSymbolFlags::Exported } }
The reexports utility can be handy for composing a single JITDylib interface by
re-exporting symbols from several other JITDylibs.
.. _Laziness:
Laziness in ORC is provided by a utility called "lazy reexports". A lazy
reexport is similar to a regular reexport or alias: It provides a new name for
an existing symbol. Unlike regular reexports however, lookups of lazy reexports
do not trigger immediate materialization of the reexported symbol. Instead, they
only trigger materialization of a function stub. This function stub is
initialized to point at a *lazy call-through*, which provides reentry into the
JIT. If the stub is called at runtime then the lazy call-through will look up
the reexported symbol (triggering materialization for it if necessary), update
the stub (to call directly to the reexported symbol on subsequent calls), and
then return via the reexported symbol. By re-using the existing symbol lookup
mechanism, lazy reexports inherit the same concurrency guarantees: calls to lazy
reexports can be made from multiple threads concurrently, and the reexported
symbol can be any state of compilation (uncompiled, already in the process of
being compiled, or already compiled) and the call will succeed. This allows
laziness to be safely mixed with features like remote compilation, concurrent
compilation, concurrent JIT'd code, and speculative compilation.
There is one other key difference between regular reexports and lazy reexports
that some clients must be aware of: The address of a lazy reexport will be
*different* from the address of the reexported symbol (whereas a regular
reexport is guaranteed to have the same address as the reexported symbol).
Clients who care about pointer equality will generally want to use the address
of the reexport as the canonical address of the reexported symbol. This will
allow the address to be taken without forcing materialization of the reexport.
Usage example:
If JITDylib ``JD`` contains definitions for symbols ``foo_body`` and
``bar_body``, we can create lazy entry points ``Foo`` and ``Bar`` in JITDylib
``JD2`` by calling:
.. code-block:: c++
auto ReexportFlags = JITSymbolFlags::Exported | JITSymbolFlags::Callable;
lazyReexports(CallThroughMgr, StubsMgr, JD,
{ Mangle("foo"), { Mangle("foo_body"), ReexportedFlags } },
{ Mangle("bar"), { Mangle("bar_body"), ReexportedFlags } }
A full example of how to use lazyReexports with the LLJIT class can be found at
Supporting Custom Compilers
.. _transitioning_orcv1_to_orcv2:
Transitioning from ORCv1 to ORCv2
Since LLVM 7.0, new ORC development work has focused on adding support for
concurrent JIT compilation. The new APIs (including new layer interfaces and
implementations, and new utilities) that support concurrency are collectively
referred to as ORCv2, and the original, non-concurrent layers and utilities
are now referred to as ORCv1.
The majority of the ORCv1 layers and utilities were renamed with a 'Legacy'
prefix in LLVM 8.0, and have deprecation warnings attached in LLVM 9.0. In LLVM
12.0 ORCv1 will be removed entirely.
Transitioning from ORCv1 to ORCv2 should be easy for most clients. Most of the
ORCv1 layers and utilities have ORCv2 counterparts [2]_ that can be directly
substituted. However there are some design differences between ORCv1 and ORCv2
to be aware of:
1. ORCv2 fully adopts the JIT-as-linker model that began with MCJIT. Modules
(and other program representations, e.g. Object Files) are no longer added
directly to JIT classes or layers. Instead, they are added to ``JITDylib``
instances *by* layers. The ``JITDylib`` determines *where* the definitions
reside, the layers determine *how* the definitions will be compiled.
Linkage relationships between ``JITDylibs`` determine how inter-module
references are resolved, and symbol resolvers are no longer used. See the
section `Design Overview`_ for more details.
Unless multiple JITDylibs are needed to model linkage relationships, ORCv1
clients should place all code in a single JITDylib.
MCJIT clients should use LLJIT (see `LLJIT and LLLazyJIT`_), and can place
code in LLJIT's default created main JITDylib (See
2. All JIT stacks now need an ``ExecutionSession`` instance. ExecutionSession
manages the string pool, error reporting, synchronization, and symbol
3. ORCv2 uses uniqued strings (``SymbolStringPtr`` instances) rather than
string values in order to reduce memory overhead and improve lookup
performance. See the subsection `How to manage symbol strings`_.
4. IR layers require ThreadSafeModule instances, rather than
std::unique_ptr<Module>s. ThreadSafeModule is a wrapper that ensures that
Modules that use the same LLVMContext are not accessed concurrently.
See `How to use ThreadSafeModule and ThreadSafeContext`_.
5. Symbol lookup is no longer handled by layers. Instead, there is a
``lookup`` method on JITDylib that takes a list of JITDylibs to scan.
.. code-block:: c++
ExecutionSession ES;
JITDylib &JD1 = ...;
JITDylib &JD2 = ...;
auto Sym = ES.lookup({&JD1, &JD2}, ES.intern("_main"));
6. Module removal is not yet supported. There is no equivalent of the
layer concept removeModule/removeObject methods. Work on resource tracking
and removal in ORCv2 is ongoing.
For code examples and suggestions of how to use the ORCv2 APIs, please see
the section `How-tos`_.
How to manage symbol strings
Symbol strings in ORC are uniqued to improve lookup performance, reduce memory
overhead, and allow symbol names to function as efficient keys. To get the
unique ``SymbolStringPtr`` for a string value, call the
``ExecutionSession::intern`` method:
.. code-block:: c++
ExecutionSession ES;
/// ...
auto MainSymbolName = ES.intern("main");
If you wish to perform lookup using the C/IR name of a symbol you will also
need to apply the platform linker-mangling before interning the string. On
Linux this mangling is a no-op, but on other platforms it usually involves
adding a prefix to the string (e.g. '_' on Darwin). The mangling scheme is
based on the DataLayout for the target. Given a DataLayout and an
ExecutionSession, you can create a MangleAndInterner function object that
will perform both jobs for you:
.. code-block:: c++
ExecutionSession ES;
const DataLayout &DL = ...;
MangleAndInterner Mangle(ES, DL);
// ...
// Portable IR-symbol-name lookup:
auto Sym = ES.lookup({&MainJD}, Mangle("main"));
How to create JITDylibs and set up linkage relationships
In ORC, all symbol definitions reside in JITDylibs. JITDylibs are created by
calling the ``ExecutionSession::createJITDylib`` method with a unique name:
.. code-block:: c++
ExecutionSession ES;
auto &JD = ES.createJITDylib("libFoo.dylib");
The JITDylib is owned by the ``ExecutionEngine`` instance and will be freed
when it is destroyed.
How to use ThreadSafeModule and ThreadSafeContext
ThreadSafeModule and ThreadSafeContext are wrappers around Modules and
LLVMContexts respectively. A ThreadSafeModule is a pair of a
std::unique_ptr<Module> and a (possibly shared) ThreadSafeContext value. A
ThreadSafeContext is a pair of a std::unique_ptr<LLVMContext> and a lock.
This design serves two purposes: providing a locking scheme and lifetime
management for LLVMContexts. The ThreadSafeContext may be locked to prevent
accidental concurrent access by two Modules that use the same LLVMContext.
The underlying LLVMContext is freed once all ThreadSafeContext values pointing
to it are destroyed, allowing the context memory to be reclaimed as soon as
the Modules referring to it are destroyed.
ThreadSafeContexts can be explicitly constructed from a
.. code-block:: c++
ThreadSafeContext TSCtx(std::make_unique<LLVMContext>());
ThreadSafeModules can be constructed from a pair of a std::unique_ptr<Module>
and a ThreadSafeContext value. ThreadSafeContext values may be shared between
multiple ThreadSafeModules:
.. code-block:: c++
ThreadSafeModule TSM1(
std::make_unique<Module>("M1", *TSCtx.getContext()), TSCtx);
ThreadSafeModule TSM2(
std::make_unique<Module>("M2", *TSCtx.getContext()), TSCtx);
Before using a ThreadSafeContext, clients should ensure that either the context
is only accessible on the current thread, or that the context is locked. In the
example above (where the context is never locked) we rely on the fact that both
``TSM1`` and ``TSM2``, and TSCtx are all created on one thread. If a context is
going to be shared between threads then it must be locked before any accessing
or creating any Modules attached to it. E.g.
.. code-block:: c++
ThreadSafeContext TSCtx(std::make_unique<LLVMContext>());
ThreadPool TP(NumThreads);
JITStack J;
for (auto &ModulePath : ModulePaths) {
[&]() {
auto Lock = TSCtx.getLock();
auto M = loadModuleOnContext(ModulePath, TSCtx.getContext());
J.addModule(ThreadSafeModule(std::move(M), TSCtx));
To make exclusive access to Modules easier to manage the ThreadSafeModule class
provides a convenience function, ``withModuleDo``, that implicitly (1) locks the
associated context, (2) runs a given function object, (3) unlocks the context,
and (3) returns the result generated by the function object. E.g.
.. code-block:: c++
ThreadSafeModule TSM = getModule(...);
// Dump the module:
size_t NumFunctionsInModule =
[](Module &M) { // <- Context locked before entering lambda.
return M.size();
} // <- Context unlocked after leaving.
Clients wishing to maximize possibilities for concurrent compilation will want
to create every new ThreadSafeModule on a new ThreadSafeContext. For this
reason a convenience constructor for ThreadSafeModule is provided that implicitly
constructs a new ThreadSafeContext value from a std::unique_ptr<LLVMContext>:
.. code-block:: c++
// Maximize concurrency opportunities by loading every module on a
// separate context.
for (const auto &IRPath : IRPaths) {
auto Ctx = std::make_unique<LLVMContext>();
auto M = std::make_unique<LLVMContext>("M", *Ctx);
CompileLayer.add(MainJD, ThreadSafeModule(std::move(M), std::move(Ctx)));
Clients who plan to run single-threaded may choose to save memory by loading
all modules on the same context:
.. code-block:: c++
// Save memory by using one context for all Modules:
ThreadSafeContext TSCtx(std::make_unique<LLVMContext>());
for (const auto &IRPath : IRPaths) {
ThreadSafeModule TSM(parsePath(IRPath, *TSCtx.getContext()), TSCtx);
CompileLayer.add(MainJD, ThreadSafeModule(std::move(TSM));
.. _ProcessAndLibrarySymbols:
How to Add Process and Library Symbols to the JITDylibs
JIT'd code typically needs access to symbols in the host program or in
supporting libraries. References to process symbols can be "baked in" to code
as it is compiled by turning external references into pre-resolved integer
constants, however this ties the JIT'd code to the current process's virtual
memory layout (meaning that it can not be cached between runs) and makes
debugging lower level program representations difficult (as all external
references are opaque integer values). A bettor solution is to maintain symbolic
external references and let the jit-linker bind them for you at runtime. To
allow the JIT linker to find these external definitions their addresses must
be added to a JITDylib that the JIT'd definitions link against.
Adding definitions for external symbols could be done using the absoluteSymbols
.. code-block:: c++
const DataLayout &DL = getDataLayout();
MangleAndInterner Mangle(ES, DL);
auto &JD = ES.createJITDylib("main");
{ Mangle("puts"), pointerToJITTargetAddress(&puts)},
{ Mangle("gets"), pointerToJITTargetAddress(&getS)}
Manually adding absolute symbols for a large or changing interface is cumbersome
however, so ORC provides an alternative to generate new definitions on demand:
*definition generators*. If a definition generator is attached to a JITDylib,
then any unsuccessful lookup on that JITDylib will fall back to calling the
definition generator, and the definition generator may choose to generate a new
definition for the missing symbols. Of particular use here is the
``DynamicLibrarySearchGenerator`` utility. This can be used to reflect the whole
exported symbol set of the process or a specific dynamic library, or a subset
of either of these determined by a predicate.
For example, to load the whole interface of a runtime library:
.. code-block:: c++
const DataLayout &DL = getDataLayout();
auto &JD = ES.createJITDylib("main");
// IR added to JD can now link against all symbols exported by the library
// at '/path/to/lib'.
CompileLayer.add(JD, loadModule(...));
Or, to expose an allowed set of symbols from the main process:
.. code-block:: c++
const DataLayout &DL = getDataLayout();
MangleAndInterner Mangle(ES, DL);
auto &JD = ES.createJITDylib("main");
DenseSet<SymbolStringPtr> AllowList({
// Use GetForCurrentProcess with a predicate function that checks the
// allowed list.
[&](const SymbolStringPtr &S) { return AllowList.count(S); }));
// IR added to JD can now link against any symbols exported by the process
// and contained in the list.
CompileLayer.add(JD, loadModule(...));
ORC is still undergoing active development. Some current and future works are
listed below.
Current Work
1. **TargetProcessControl: Improvements to in-tree support for out-of-process
The ``TargetProcessControl`` API provides various operations on the JIT
target process (the one which will execute the JIT'd code), including
memory allocation, memory writes, function execution, and process queries
(e.g. for the target triple). By targeting this API new components can be
developed which will work equally well for in-process and out-of-process
2. **ORC RPC based TargetProcessControl implementation**
An ORC RPC based implementation of the ``TargetProcessControl`` API is
currently under development to enable easy out-of-process JITing via
file descriptors / sockets.
3. **Core State Machine Cleanup**
The core ORC state machine is currently implemented between JITDylib and
ExecutionSession. Methods are slowly being moved to `ExecutionSession`. This
will tidy up the code base, and also allow us to support asynchronous removal
of JITDylibs (in practice deleting an associated state object in
ExecutionSession and leaving the JITDylib instance in a defunct state until
all references to it have been released).
Near Future Work
1. **ORC JIT Runtime Libraries**
We need a runtime library for JIT'd code. This would include things like
TLS registration, reentry functions, registration code for language runtimes
(e.g. Objective C and Swift) and other JIT specific runtime code. This should
be built in a similar manner to compiler-rt (possibly even as part of it).
2. **Remote jit_dlopen / jit_dlclose**
To more fully mimic the environment that static programs operate in we would
like JIT'd code to be able to "dlopen" and "dlclose" JITDylibs, running all of
their initializers/deinitializers on the current thread. This would require
support from the runtime library described above.
3. **Debugging support**
ORC currently supports the GDBRegistrationListener API when using RuntimeDyld
as the underlying JIT linker. We will need a new solution for JITLink based
Further Future Work
1. **Speculative Compilation**
ORC's support for concurrent compilation allows us to easily enable
*speculative* JIT compilation: compilation of code that is not needed yet,
but which we have reason to believe will be needed in the future. This can be
used to hide compile latency and improve JIT throughput. A proof-of-concept
example of speculative compilation with ORC has already been developed (see
``llvm/examples/SpeculativeJIT``). Future work on this is likely to focus on
re-using and improving existing profiling support (currently used by PGO) to
feed speculation decisions, as well as built-in tools to simplify use of
speculative compilation.
.. [1] Formats/architectures vary in terms of supported features. MachO and
ELF tend to have better support than COFF. Patches very welcome!
.. [2] The ``LazyEmittingLayer``, ``RemoteObjectClientLayer`` and
``RemoteObjectServerLayer`` do not have counterparts in the new
system. In the case of ``LazyEmittingLayer`` it was simply no longer
needed: in ORCv2, deferring compilation until symbols are looked up is
the default. The removal of ``RemoteObjectClientLayer`` and
``RemoteObjectServerLayer`` means that JIT stacks can no longer be split
across processes, however this functionality appears not to have been
.. [3] Weak definitions are currently handled correctly within dylibs, but if
multiple dylibs provide a weak definition of a symbol then each will end
up with its own definition (similar to how weak definitions are handled
in Windows DLLs). This will be fixed in the future.