LLVM Programmer's Manual
  1. Introduction
  2. General Information
  3. Important and useful LLVM APIs
  4. Picking the Right Data Structure for a Task
  5. Helpful Hints for Common Operations
  6. Advanced Topics
  7. The Core LLVM Class Hierarchy Reference

Written by Chris Lattner, Dinakar Dhurjati, Gabor Greif, Joel Stanley and Reid Spencer


This document is meant to highlight some of the important classes and interfaces available in the LLVM source-base. This manual is not intended to explain what LLVM is, how it works, and what LLVM code looks like. It assumes that you know the basics of LLVM and are interested in writing transformations or otherwise analyzing or manipulating the code.

This document should get you oriented so that you can find your way in the continuously growing source code that makes up the LLVM infrastructure. Note that this manual is not intended to serve as a replacement for reading the source code, so if you think there should be a method in one of these classes to do something, but it's not listed, check the source. Links to the doxygen sources are provided to make this as easy as possible.

The first section of this document describes general information that is useful to know when working in the LLVM infrastructure, and the second describes the Core LLVM classes. In the future this manual will be extended with information describing how to use extension libraries, such as dominator information, CFG traversal routines, and useful utilities like the InstVisitor template.

General Information

This section contains general information that is useful if you are working in the LLVM source-base, but that isn't specific to any particular API.

The C++ Standard Template Library

LLVM makes heavy use of the C++ Standard Template Library (STL), perhaps much more than you are used to, or have seen before. Because of this, you might want to do a little background reading in the techniques used and capabilities of the library. There are many good pages that discuss the STL, and several books on the subject that you can get, so it will not be discussed in this document.

Here are some useful links:

  1. Dinkumware C++ Library reference - an excellent reference for the STL and other parts of the standard C++ library.
  2. C++ In a Nutshell - This is an O'Reilly book in the making. It has a decent Standard Library Reference that rivals Dinkumware's, and is unfortunately no longer free since the book has been published.
  3. C++ Frequently Asked Questions
  4. SGI's STL Programmer's Guide - Contains a useful Introduction to the STL.
  5. Bjarne Stroustrup's C++ Page
  6. Bruce Eckel's Thinking in C++, 2nd ed. Volume 2 Revision 4.0 (even better, get the book).

You are also encouraged to take a look at the LLVM Coding Standards guide which focuses on how to write maintainable code more than where to put your curly braces.

Other useful references
  1. CVS Branch and Tag Primer
  2. Using static and shared libraries across platforms
Important and useful LLVM APIs

Here we highlight some LLVM APIs that are generally useful and good to know about when writing transformations.

The isa<>, cast<> and dyn_cast<> templates

The LLVM source-base makes extensive use of a custom form of RTTI. These templates have many similarities to the C++ dynamic_cast<> operator, but they don't have some drawbacks (primarily stemming from the fact that dynamic_cast<> only works on classes that have a v-table). Because they are used so often, you must know what they do and how they work. All of these templates are defined in the llvm/Support/Casting.h file (note that you very rarely have to include this file directly).


The isa<> operator works exactly like the Java "instanceof" operator. It returns true or false depending on whether a reference or pointer points to an instance of the specified class. This can be very useful for constraint checking of various sorts (example below).


The cast<> operator is a "checked cast" operation. It converts a pointer or reference from a base class to a derived class, causing an assertion failure if it is not really an instance of the right type. This should be used in cases where you have some information that makes you believe that something is of the right type. An example of the isa<> and cast<> template is:

static bool isLoopInvariant(const Value *V, const Loop *L) {
  if (isa<Constant>(V) || isa<Argument>(V) || isa<GlobalValue>(V))
    return true;

  // Otherwise, it must be an instruction...
  return !L->contains(cast<Instruction>(V)->getParent());

Note that you should not use an isa<> test followed by a cast<>, for that use the dyn_cast<> operator.


The dyn_cast<> operator is a "checking cast" operation. It checks to see if the operand is of the specified type, and if so, returns a pointer to it (this operator does not work with references). If the operand is not of the correct type, a null pointer is returned. Thus, this works very much like the dynamic_cast<> operator in C++, and should be used in the same circumstances. Typically, the dyn_cast<> operator is used in an if statement or some other flow control statement like this:

if (AllocationInst *AI = dyn_cast<AllocationInst>(Val)) {
  // ...

This form of the if statement effectively combines together a call to isa<> and a call to cast<> into one statement, which is very convenient.

Note that the dyn_cast<> operator, like C++'s dynamic_cast<> or Java's instanceof operator, can be abused. In particular, you should not use big chained if/then/else blocks to check for lots of different variants of classes. If you find yourself wanting to do this, it is much cleaner and more efficient to use the InstVisitor class to dispatch over the instruction type directly.


The cast_or_null<> operator works just like the cast<> operator, except that it allows for a null pointer as an argument (which it then propagates). This can sometimes be useful, allowing you to combine several null checks into one.


The dyn_cast_or_null<> operator works just like the dyn_cast<> operator, except that it allows for a null pointer as an argument (which it then propagates). This can sometimes be useful, allowing you to combine several null checks into one.

These five templates can be used with any classes, whether they have a v-table or not. To add support for these templates, you simply need to add classof static methods to the class you are interested casting to. Describing this is currently outside the scope of this document, but there are lots of examples in the LLVM source base.

The DEBUG() macro and -debug option

Often when working on your pass you will put a bunch of debugging printouts and other code into your pass. After you get it working, you want to remove it, but you may need it again in the future (to work out new bugs that you run across).

Naturally, because of this, you don't want to delete the debug printouts, but you don't want them to always be noisy. A standard compromise is to comment them out, allowing you to enable them if you need them in the future.

The "llvm/Support/Debug.h" file provides a macro named DEBUG() that is a much nicer solution to this problem. Basically, you can put arbitrary code into the argument of the DEBUG macro, and it is only executed if 'opt' (or any other tool) is run with the '-debug' command line argument:

DOUT << "I am here!\n";

Then you can run your pass like this:

$ opt < a.bc > /dev/null -mypass
<no output>
$ opt < a.bc > /dev/null -mypass -debug
I am here!

Using the DEBUG() macro instead of a home-brewed solution allows you to not have to create "yet another" command line option for the debug output for your pass. Note that DEBUG() macros are disabled for optimized builds, so they do not cause a performance impact at all (for the same reason, they should also not contain side-effects!).

One additional nice thing about the DEBUG() macro is that you can enable or disable it directly in gdb. Just use "set DebugFlag=0" or "set DebugFlag=1" from the gdb if the program is running. If the program hasn't been started yet, you can always just run it with -debug.

Fine grained debug info with DEBUG_TYPE and the -debug-only option

Sometimes you may find yourself in a situation where enabling -debug just turns on too much information (such as when working on the code generator). If you want to enable debug information with more fine-grained control, you define the DEBUG_TYPE macro and the -debug only option as follows:

DOUT << "No debug type\n";
#undef  DEBUG_TYPE
#define DEBUG_TYPE "foo"
DOUT << "'foo' debug type\n";
#undef  DEBUG_TYPE
#define DEBUG_TYPE "bar"
DOUT << "'bar' debug type\n";
#undef  DEBUG_TYPE
#define DEBUG_TYPE ""
DOUT << "No debug type (2)\n";

Then you can run your pass like this:

$ opt < a.bc > /dev/null -mypass
<no output>
$ opt < a.bc > /dev/null -mypass -debug
No debug type
'foo' debug type
'bar' debug type
No debug type (2)
$ opt < a.bc > /dev/null -mypass -debug-only=foo
'foo' debug type
$ opt < a.bc > /dev/null -mypass -debug-only=bar
'bar' debug type

Of course, in practice, you should only set DEBUG_TYPE at the top of a file, to specify the debug type for the entire module (if you do this before you #include "llvm/Support/Debug.h", you don't have to insert the ugly #undef's). Also, you should use names more meaningful than "foo" and "bar", because there is no system in place to ensure that names do not conflict. If two different modules use the same string, they will all be turned on when the name is specified. This allows, for example, all debug information for instruction scheduling to be enabled with -debug-type=InstrSched, even if the source lives in multiple files.

The Statistic class & -stats option

The "llvm/ADT/Statistic.h" file provides a class named Statistic that is used as a unified way to keep track of what the LLVM compiler is doing and how effective various optimizations are. It is useful to see what optimizations are contributing to making a particular program run faster.

Often you may run your pass on some big program, and you're interested to see how many times it makes a certain transformation. Although you can do this with hand inspection, or some ad-hoc method, this is a real pain and not very useful for big programs. Using the Statistic class makes it very easy to keep track of this information, and the calculated information is presented in a uniform manner with the rest of the passes being executed.

There are many examples of Statistic uses, but the basics of using it are as follows:

  1. Define your statistic like this:

    #define DEBUG_TYPE "mypassname"   // This goes before any #includes.
    STATISTIC(NumXForms, "The # of times I did stuff");

    The STATISTIC macro defines a static variable, whose name is specified by the first argument. The pass name is taken from the DEBUG_TYPE macro, and the description is taken from the second argument. The variable defined ("NumXForms" in this case) acts like an unsigned integer.

  2. Whenever you make a transformation, bump the counter:

    ++NumXForms;   // I did stuff!

That's all you have to do. To get 'opt' to print out the statistics gathered, use the '-stats' option:

$ opt -stats -mypassname < program.bc > /dev/null
... statistics output ...

When running opt on a C file from the SPEC benchmark suite, it gives a report that looks like this:

   7646 bitcodewriter   - Number of normal instructions
    725 bitcodewriter   - Number of oversized instructions
 129996 bitcodewriter   - Number of bitcode bytes written
   2817 raise           - Number of insts DCEd or constprop'd
   3213 raise           - Number of cast-of-self removed
   5046 raise           - Number of expression trees converted
     75 raise           - Number of other getelementptr's formed
    138 raise           - Number of load/store peepholes
     42 deadtypeelim    - Number of unused typenames removed from symtab
    392 funcresolve     - Number of varargs functions resolved
     27 globaldce       - Number of global variables removed
      2 adce            - Number of basic blocks removed
    134 cee             - Number of branches revectored
     49 cee             - Number of setcc instruction eliminated
    532 gcse            - Number of loads removed
   2919 gcse            - Number of instructions removed
     86 indvars         - Number of canonical indvars added
     87 indvars         - Number of aux indvars removed
     25 instcombine     - Number of dead inst eliminate
    434 instcombine     - Number of insts combined
    248 licm            - Number of load insts hoisted
   1298 licm            - Number of insts hoisted to a loop pre-header
      3 licm            - Number of insts hoisted to multiple loop preds (bad, no loop pre-header)
     75 mem2reg         - Number of alloca's promoted
   1444 cfgsimplify     - Number of blocks simplified

Obviously, with so many optimizations, having a unified framework for this stuff is very nice. Making your pass fit well into the framework makes it more maintainable and useful.

Viewing graphs while debugging code

Several of the important data structures in LLVM are graphs: for example CFGs made out of LLVM BasicBlocks, CFGs made out of LLVM MachineBasicBlocks, and Instruction Selection DAGs. In many cases, while debugging various parts of the compiler, it is nice to instantly visualize these graphs.

LLVM provides several callbacks that are available in a debug build to do exactly that. If you call the Function::viewCFG() method, for example, the current LLVM tool will pop up a window containing the CFG for the function where each basic block is a node in the graph, and each node contains the instructions in the block. Similarly, there also exists Function::viewCFGOnly() (does not include the instructions), the MachineFunction::viewCFG() and MachineFunction::viewCFGOnly(), and the SelectionDAG::viewGraph() methods. Within GDB, for example, you can usually use something like call DAG.viewGraph() to pop up a window. Alternatively, you can sprinkle calls to these functions in your code in places you want to debug.

Getting this to work requires a small amount of configuration. On Unix systems with X11, install the graphviz toolkit, and make sure 'dot' and 'gv' are in your path. If you are running on Mac OS/X, download and install the Mac OS/X Graphviz program, and add /Applications/Graphviz.app/Contents/MacOS/ (or wherever you install it) to your path. Once in your system and path are set up, rerun the LLVM configure script and rebuild LLVM to enable this functionality.

SelectionDAG has been extended to make it easier to locate interesting nodes in large complex graphs. From gdb, if you call DAG.setGraphColor(node, "color"), then the next call DAG.viewGraph() would highlight the node in the specified color (choices of colors can be found at colors.) More complex node attributes can be provided with call DAG.setGraphAttrs(node, "attributes") (choices can be found at Graph Attributes.) If you want to restart and clear all the current graph attributes, then you can call DAG.clearGraphAttrs().

Picking the Right Data Structure for a Task

LLVM has a plethora of data structures in the llvm/ADT/ directory, and we commonly use STL data structures. This section describes the trade-offs you should consider when you pick one.

The first step is a choose your own adventure: do you want a sequential container, a set-like container, or a map-like container? The most important thing when choosing a container is the algorithmic properties of how you plan to access the container. Based on that, you should use:

Once the proper category of container is determined, you can fine tune the memory use, constant factors, and cache behaviors of access by intelligently picking a member of the category. Note that constant factors and cache behavior can be a big deal. If you have a vector that usually only contains a few elements (but could contain many), for example, it's much better to use SmallVector than vector . Doing so avoids (relatively) expensive malloc/free calls, which dwarf the cost of adding the elements to the container.

Sequential Containers (std::vector, std::list, etc)
There are a variety of sequential containers available for you, based on your needs. Pick the first in this section that will do what you want.
Fixed Size Arrays

Fixed size arrays are very simple and very fast. They are good if you know exactly how many elements you have, or you have a (low) upper bound on how many you have.

Heap Allocated Arrays

Heap allocated arrays (new[] + delete[]) are also simple. They are good if the number of elements is variable, if you know how many elements you will need before the array is allocated, and if the array is usually large (if not, consider a SmallVector). The cost of a heap allocated array is the cost of the new/delete (aka malloc/free). Also note that if you are allocating an array of a type with a constructor, the constructor and destructors will be run for every element in the array (re-sizable vectors only construct those elements actually used).


SmallVector<Type, N> is a simple class that looks and smells just like vector<Type>: it supports efficient iteration, lays out elements in memory order (so you can do pointer arithmetic between elements), supports efficient push_back/pop_back operations, supports efficient random access to its elements, etc.

The advantage of SmallVector is that it allocates space for some number of elements (N) in the object itself. Because of this, if the SmallVector is dynamically smaller than N, no malloc is performed. This can be a big win in cases where the malloc/free call is far more expensive than the code that fiddles around with the elements.

This is good for vectors that are "usually small" (e.g. the number of predecessors/successors of a block is usually less than 8). On the other hand, this makes the size of the SmallVector itself large, so you don't want to allocate lots of them (doing so will waste a lot of space). As such, SmallVectors are most useful when on the stack.

SmallVector also provides a nice portable and efficient replacement for alloca.


std::vector is well loved and respected. It is useful when SmallVector isn't: when the size of the vector is often large (thus the small optimization will rarely be a benefit) or if you will be allocating many instances of the vector itself (which would waste space for elements that aren't in the container). vector is also useful when interfacing with code that expects vectors :).

One worthwhile note about std::vector: avoid code like this:

for ( ... ) {
   std::vector<foo> V;
   use V;

Instead, write this as:

std::vector<foo> V;
for ( ... ) {
   use V;

Doing so will save (at least) one heap allocation and free per iteration of the loop.


std::deque is, in some senses, a generalized version of std::vector. Like std::vector, it provides constant time random access and other similar properties, but it also provides efficient access to the front of the list. It does not guarantee continuity of elements within memory.

In exchange for this extra flexibility, std::deque has significantly higher constant factor costs than std::vector. If possible, use std::vector or something cheaper.


std::list is an extremely inefficient class that is rarely useful. It performs a heap allocation for every element inserted into it, thus having an extremely high constant factor, particularly for small data types. std::list also only supports bidirectional iteration, not random access iteration.

In exchange for this high cost, std::list supports efficient access to both ends of the list (like std::deque, but unlike std::vector or SmallVector). In addition, the iterator invalidation characteristics of std::list are stronger than that of a vector class: inserting or removing an element into the list does not invalidate iterator or pointers to other elements in the list.


ilist<T> implements an 'intrusive' doubly-linked list. It is intrusive, because it requires the element to store and provide access to the prev/next pointers for the list.

ilist has the same drawbacks as std::list, and additionally requires an ilist_traits implementation for the element type, but it provides some novel characteristics. In particular, it can efficiently store polymorphic objects, the traits class is informed when an element is inserted or removed from the list, and ilists are guaranteed to support a constant-time splice operation.

These properties are exactly what we want for things like Instructions and basic blocks, which is why these are implemented with ilists.

Other Sequential Container options

Other STL containers are available, such as std::string.

There are also various STL adapter classes such as std::queue, std::priority_queue, std::stack, etc. These provide simplified access to an underlying container but don't affect the cost of the container itself.

Set-Like Containers (std::set, SmallSet, SetVector, etc)

Set-like containers are useful when you need to canonicalize multiple values into a single representation. There are several different choices for how to do this, providing various trade-offs.

A sorted 'vector'

If you intend to insert a lot of elements, then do a lot of queries, a great approach is to use a vector (or other sequential container) with std::sort+std::unique to remove duplicates. This approach works really well if your usage pattern has these two distinct phases (insert then query), and can be coupled with a good choice of sequential container.

This combination provides the several nice properties: the result data is contiguous in memory (good for cache locality), has few allocations, is easy to address (iterators in the final vector are just indices or pointers), and can be efficiently queried with a standard binary or radix search.


If you have a set-like data structure that is usually small and whose elements are reasonably small, a SmallSet<Type, N> is a good choice. This set has space for N elements in place (thus, if the set is dynamically smaller than N, no malloc traffic is required) and accesses them with a simple linear search. When the set grows beyond 'N' elements, it allocates a more expensive representation that guarantees efficient access (for most types, it falls back to std::set, but for pointers it uses something far better, SmallPtrSet).

The magic of this class is that it handles small sets extremely efficiently, but gracefully handles extremely large sets without loss of efficiency. The drawback is that the interface is quite small: it supports insertion, queries and erasing, but does not support iteration.


SmallPtrSet has all the advantages of SmallSet (and a SmallSet of pointers is transparently implemented with a SmallPtrSet), but also supports iterators. If more than 'N' insertions are performed, a single quadratically probed hash table is allocated and grows as needed, providing extremely efficient access (constant time insertion/deleting/queries with low constant factors) and is very stingy with malloc traffic.

Note that, unlike std::set, the iterators of SmallPtrSet are invalidated whenever an insertion occurs. Also, the values visited by the iterators are not visited in sorted order.


DenseSet is a simple quadratically probed hash table. It excels at supporting small values: it uses a single allocation to hold all of the pairs that are currently inserted in the set. DenseSet is a great way to unique small values that are not simple pointers (use SmallPtrSet for pointers). Note that DenseSet has the same requirements for the value type that DenseMap has.


FoldingSet is an aggregate class that is really good at uniquing expensive-to-create or polymorphic objects. It is a combination of a chained hash table with intrusive links (uniqued objects are required to inherit from FoldingSetNode) that uses SmallVector as part of its ID process.

Consider a case where you want to implement a "getOrCreateFoo" method for a complex object (for example, a node in the code generator). The client has a description of *what* it wants to generate (it knows the opcode and all the operands), but we don't want to 'new' a node, then try inserting it into a set only to find out it already exists, at which point we would have to delete it and return the node that already exists.

To support this style of client, FoldingSet perform a query with a FoldingSetNodeID (which wraps SmallVector) that can be used to describe the element that we want to query for. The query either returns the element matching the ID or it returns an opaque ID that indicates where insertion should take place. Construction of the ID usually does not require heap traffic.

Because FoldingSet uses intrusive links, it can support polymorphic objects in the set (for example, you can have SDNode instances mixed with LoadSDNodes). Because the elements are individually allocated, pointers to the elements are stable: inserting or removing elements does not invalidate any pointers to other elements.


std::set is a reasonable all-around set class, which is decent at many things but great at nothing. std::set allocates memory for each element inserted (thus it is very malloc intensive) and typically stores three pointers per element in the set (thus adding a large amount of per-element space overhead). It offers guaranteed log(n) performance, which is not particularly fast from a complexity standpoint (particularly if the elements of the set are expensive to compare, like strings), and has extremely high constant factors for lookup, insertion and removal.

The advantages of std::set are that its iterators are stable (deleting or inserting an element from the set does not affect iterators or pointers to other elements) and that iteration over the set is guaranteed to be in sorted order. If the elements in the set are large, then the relative overhead of the pointers and malloc traffic is not a big deal, but if the elements of the set are small, std::set is almost never a good choice.


LLVM's SetVector<Type> is an adapter class that combines your choice of a set-like container along with a Sequential Container. The important property that this provides is efficient insertion with uniquing (duplicate elements are ignored) with iteration support. It implements this by inserting elements into both a set-like container and the sequential container, using the set-like container for uniquing and the sequential container for iteration.

The difference between SetVector and other sets is that the order of iteration is guaranteed to match the order of insertion into the SetVector. This property is really important for things like sets of pointers. Because pointer values are non-deterministic (e.g. vary across runs of the program on different machines), iterating over the pointers in the set will not be in a well-defined order.

The drawback of SetVector is that it requires twice as much space as a normal set and has the sum of constant factors from the set-like container and the sequential container that it uses. Use it *only* if you need to iterate over the elements in a deterministic order. SetVector is also expensive to delete elements out of (linear time), unless you use it's "pop_back" method, which is faster.

SetVector is an adapter class that defaults to using std::vector and std::set for the underlying containers, so it is quite expensive. However, "llvm/ADT/SetVector.h" also provides a SmallSetVector class, which defaults to using a SmallVector and SmallSet of a specified size. If you use this, and if your sets are dynamically smaller than N, you will save a lot of heap traffic.


UniqueVector is similar to SetVector, but it retains a unique ID for each element inserted into the set. It internally contains a map and a vector, and it assigns a unique ID for each value inserted into the set.

UniqueVector is very expensive: its cost is the sum of the cost of maintaining both the map and vector, it has high complexity, high constant factors, and produces a lot of malloc traffic. It should be avoided.

Other Set-Like Container Options

The STL provides several other options, such as std::multiset and the various "hash_set" like containers (whether from C++ TR1 or from the SGI library).

std::multiset is useful if you're not interested in elimination of duplicates, but has all the drawbacks of std::set. A sorted vector (where you don't delete duplicate entries) or some other approach is almost always better.

The various hash_set implementations (exposed portably by "llvm/ADT/hash_set") is a simple chained hashtable. This algorithm is as malloc intensive as std::set (performing an allocation for each element inserted, thus having really high constant factors) but (usually) provides O(1) insertion/deletion of elements. This can be useful if your elements are large (thus making the constant-factor cost relatively low) or if comparisons are expensive. Element iteration does not visit elements in a useful order.

Map-Like Containers (std::map, DenseMap, etc)
Map-like containers are useful when you want to associate data to a key. As usual, there are a lot of different ways to do this. :)
A sorted 'vector'

If your usage pattern follows a strict insert-then-query approach, you can trivially use the same approach as sorted vectors for set-like containers. The only difference is that your query function (which uses std::lower_bound to get efficient log(n) lookup) should only compare the key, not both the key and value. This yields the same advantages as sorted vectors for sets.


Strings are commonly used as keys in maps, and they are difficult to support efficiently: they are variable length, inefficient to hash and compare when long, expensive to copy, etc. StringMap is a specialized container designed to cope with these issues. It supports mapping an arbitrary range of bytes to an arbitrary other object.

The StringMap implementation uses a quadratically-probed hash table, where the buckets store a pointer to the heap allocated entries (and some other stuff). The entries in the map must be heap allocated because the strings are variable length. The string data (key) and the element object (value) are stored in the same allocation with the string data immediately after the element object. This container guarantees the "(char*)(&Value+1)" points to the key string for a value.

The StringMap is very fast for several reasons: quadratic probing is very cache efficient for lookups, the hash value of strings in buckets is not recomputed when lookup up an element, StringMap rarely has to touch the memory for unrelated objects when looking up a value (even when hash collisions happen), hash table growth does not recompute the hash values for strings already in the table, and each pair in the map is store in a single allocation (the string data is stored in the same allocation as the Value of a pair).

StringMap also provides query methods that take byte ranges, so it only ever copies a string if a value is inserted into the table.


IndexedMap is a specialized container for mapping small dense integers (or values that can be mapped to small dense integers) to some other type. It is internally implemented as a vector with a mapping function that maps the keys to the dense integer range.

This is useful for cases like virtual registers in the LLVM code generator: they have a dense mapping that is offset by a compile-time constant (the first virtual register ID).


DenseMap is a simple quadratically probed hash table. It excels at supporting small keys and values: it uses a single allocation to hold all of the pairs that are currently inserted in the map. DenseMap is a great way to map pointers to pointers, or map other small types to each other.

There are several aspects of DenseMap that you should be aware of, however. The iterators in a densemap are invalidated whenever an insertion occurs, unlike map. Also, because DenseMap allocates space for a large number of key/value pairs (it starts with 64 by default), it will waste a lot of space if your keys or values are large. Finally, you must implement a partial specialization of DenseMapInfo for the key that you want, if it isn't already supported. This is required to tell DenseMap about two special marker values (which can never be inserted into the map) that it needs internally.


std::map has similar characteristics to std::set: it uses a single allocation per pair inserted into the map, it offers log(n) lookup with an extremely large constant factor, imposes a space penalty of 3 pointers per pair in the map, etc.

std::map is most useful when your keys or values are very large, if you need to iterate over the collection in sorted order, or if you need stable iterators into the map (i.e. they don't get invalidated if an insertion or deletion of another element takes place).

Other Map-Like Container Options

The STL provides several other options, such as std::multimap and the various "hash_map" like containers (whether from C++ TR1 or from the SGI library).

std::multimap is useful if you want to map a key to multiple values, but has all the drawbacks of std::map. A sorted vector or some other approach is almost always better.

The various hash_map implementations (exposed portably by "llvm/ADT/hash_map") are simple chained hash tables. This algorithm is as malloc intensive as std::map (performing an allocation for each element inserted, thus having really high constant factors) but (usually) provides O(1) insertion/deletion of elements. This can be useful if your elements are large (thus making the constant-factor cost relatively low) or if comparisons are expensive. Element iteration does not visit elements in a useful order.

Bit storage containers (BitVector, SparseBitVector)

Unlike the other containers, there are only two bit storage containers, and choosing when to use each is relatively straightforward.

One additional option is std::vector<bool>: we discourage its use for two reasons 1) the implementation in many common compilers (e.g. commonly available versions of GCC) is extremely inefficient and 2) the C++ standards committee is likely to deprecate this container and/or change it significantly somehow. In any case, please don't use it.


The BitVector container provides a fixed size set of bits for manipulation. It supports individual bit setting/testing, as well as set operations. The set operations take time O(size of bitvector), but operations are performed one word at a time, instead of one bit at a time. This makes the BitVector very fast for set operations compared to other containers. Use the BitVector when you expect the number of set bits to be high (IE a dense set).


The SparseBitVector container is much like BitVector, with one major difference: Only the bits that are set, are stored. This makes the SparseBitVector much more space efficient than BitVector when the set is sparse, as well as making set operations O(number of set bits) instead of O(size of universe). The downside to the SparseBitVector is that setting and testing of random bits is O(N), and on large SparseBitVectors, this can be slower than BitVector. In our implementation, setting or testing bits in sorted order (either forwards or reverse) is O(1) worst case. Testing and setting bits within 128 bits (depends on size) of the current bit is also O(1). As a general statement, testing/setting bits in a SparseBitVector is O(distance away from last set bit).

Helpful Hints for Common Operations

This section describes how to perform some very simple transformations of LLVM code. This is meant to give examples of common idioms used, showing the practical side of LLVM transformations.

Because this is a "how-to" section, you should also read about the main classes that you will be working with. The Core LLVM Class Hierarchy Reference contains details and descriptions of the main classes that you should know about.

Basic Inspection and Traversal Routines

The LLVM compiler infrastructure have many different data structures that may be traversed. Following the example of the C++ standard template library, the techniques used to traverse these various data structures are all basically the same. For a enumerable sequence of values, the XXXbegin() function (or method) returns an iterator to the start of the sequence, the XXXend() function returns an iterator pointing to one past the last valid element of the sequence, and there is some XXXiterator data type that is common between the two operations.

Because the pattern for iteration is common across many different aspects of the program representation, the standard template library algorithms may be used on them, and it is easier to remember how to iterate. First we show a few common examples of the data structures that need to be traversed. Other data structures are traversed in very similar ways.

Iterating over the BasicBlocks in a Function

It's quite common to have a Function instance that you'd like to transform in some way; in particular, you'd like to manipulate its BasicBlocks. To facilitate this, you'll need to iterate over all of the BasicBlocks that constitute the Function. The following is an example that prints the name of a BasicBlock and the number of Instructions it contains:

// func is a pointer to a Function instance
for (Function::iterator i = func->begin(), e = func->end(); i != e; ++i)
  // Print out the name of the basic block if it has one, and then the
  // number of instructions that it contains
  llvm::cerr << "Basic block (name=" << i->getName() << ") has "
             << i->size() << " instructions.\n";

Note that i can be used as if it were a pointer for the purposes of invoking member functions of the Instruction class. This is because the indirection operator is overloaded for the iterator classes. In the above code, the expression i->size() is exactly equivalent to (*i).size() just like you'd expect.

Iterating over the Instructions in a BasicBlock

Just like when dealing with BasicBlocks in Functions, it's easy to iterate over the individual instructions that make up BasicBlocks. Here's a code snippet that prints out each instruction in a BasicBlock:

// blk is a pointer to a BasicBlock instance
for (BasicBlock::iterator i = blk->begin(), e = blk->end(); i != e; ++i)
   // The next statement works since operator<<(ostream&,...)
   // is overloaded for Instruction&
   llvm::cerr << *i << "\n";

However, this isn't really the best way to print out the contents of a BasicBlock! Since the ostream operators are overloaded for virtually anything you'll care about, you could have just invoked the print routine on the basic block itself: llvm::cerr << *blk << "\n";.

Iterating over the Instructions in a Function

If you're finding that you commonly iterate over a Function's BasicBlocks and then that BasicBlock's Instructions, InstIterator should be used instead. You'll need to include llvm/Support/InstIterator.h, and then instantiate InstIterators explicitly in your code. Here's a small example that shows how to dump all instructions in a function to the standard error stream:

#include "llvm/Support/InstIterator.h"

// F is a pointer to a Function instance
for (inst_iterator I = inst_begin(F), E = inst_end(F); I != E; ++I)
  llvm::cerr << *I << "\n";

Easy, isn't it? You can also use InstIterators to fill a work list with its initial contents. For example, if you wanted to initialize a work list to contain all instructions in a Function F, all you would need to do is something like:

std::set<Instruction*> worklist;
// or better yet, SmallPtrSet<Instruction*, 64> worklist;

for (inst_iterator I = inst_begin(F), E = inst_end(F); I != E; ++I)

The STL set worklist would now contain all instructions in the Function pointed to by F.

Turning an iterator into a class pointer (and vice-versa)

Sometimes, it'll be useful to grab a reference (or pointer) to a class instance when all you've got at hand is an iterator. Well, extracting a reference or a pointer from an iterator is very straight-forward. Assuming that i is a BasicBlock::iterator and j is a BasicBlock::const_iterator:

Instruction& inst = *i;   // Grab reference to instruction reference
Instruction* pinst = &*i; // Grab pointer to instruction reference
const Instruction& inst = *j;

However, the iterators you'll be working with in the LLVM framework are special: they will automatically convert to a ptr-to-instance type whenever they need to. Instead of dereferencing the iterator and then taking the address of the result, you can simply assign the iterator to the proper pointer type and you get the dereference and address-of operation as a result of the assignment (behind the scenes, this is a result of overloading casting mechanisms). Thus the last line of the last example,

Instruction *pinst = &*i;

is semantically equivalent to

Instruction *pinst = i;

It's also possible to turn a class pointer into the corresponding iterator, and this is a constant time operation (very efficient). The following code snippet illustrates use of the conversion constructors provided by LLVM iterators. By using these, you can explicitly grab the iterator of something without actually obtaining it via iteration over some structure:

void printNextInstruction(Instruction* inst) {
  BasicBlock::iterator it(inst);
  ++it; // After this line, it refers to the instruction after *inst
  if (it != inst->getParent()->end()) llvm::cerr << *it << "\n";
Finding call sites: a slightly more complex example

Say that you're writing a FunctionPass and would like to count all the locations in the entire module (that is, across every Function) where a certain function (i.e., some Function*) is already in scope. As you'll learn later, you may want to use an InstVisitor to accomplish this in a much more straight-forward manner, but this example will allow us to explore how you'd do it if you didn't have InstVisitor around. In pseudo-code, this is what we want to do:

initialize callCounter to zero
for each Function f in the Module
  for each BasicBlock b in f
    for each Instruction i in b
      if (i is a CallInst and calls the given function)
        increment callCounter

And the actual code is (remember, because we're writing a FunctionPass, our FunctionPass-derived class simply has to override the runOnFunction method):

Function* targetFunc = ...;

class OurFunctionPass : public FunctionPass {
    OurFunctionPass(): callCounter(0) { }

    virtual runOnFunction(Function& F) {
      for (Function::iterator b = F.begin(), be = F.end(); b != be; ++b) {
        for (BasicBlock::iterator i = b->begin(), ie = b->end(); i != ie; ++i) {
          if (CallInst* callInst = dyn_cast<CallInst>(&*i)) {
            // We know we've encountered a call instruction, so we
            // need to determine if it's a call to the
            // function pointed to by m_func or not.
            if (callInst->getCalledFunction() == targetFunc)

    unsigned callCounter;
Treating calls and invokes the same way

You may have noticed that the previous example was a bit oversimplified in that it did not deal with call sites generated by 'invoke' instructions. In this, and in other situations, you may find that you want to treat CallInsts and InvokeInsts the same way, even though their most-specific common base class is Instruction, which includes lots of less closely-related things. For these cases, LLVM provides a handy wrapper class called CallSite. It is essentially a wrapper around an Instruction pointer, with some methods that provide functionality common to CallInsts and InvokeInsts.

This class has "value semantics": it should be passed by value, not by reference and it should not be dynamically allocated or deallocated using operator new or operator delete. It is efficiently copyable, assignable and constructable, with costs equivalents to that of a bare pointer. If you look at its definition, it has only a single pointer member.

Iterating over def-use & use-def chains

Frequently, we might have an instance of the Value Class and we want to determine which Users use the Value. The list of all Users of a particular Value is called a def-use chain. For example, let's say we have a Function* named F to a particular function foo. Finding all of the instructions that use foo is as simple as iterating over the def-use chain of F:

Function *F = ...;

for (Value::use_iterator i = F->use_begin(), e = F->use_end(); i != e; ++i)
  if (Instruction *Inst = dyn_cast<Instruction>(*i)) {
    llvm::cerr << "F is used in instruction:\n";
    llvm::cerr << *Inst << "\n";

Alternately, it's common to have an instance of the User Class and need to know what Values are used by it. The list of all Values used by a User is known as a use-def chain. Instances of class Instruction are common Users, so we might want to iterate over all of the values that a particular instruction uses (that is, the operands of the particular Instruction):

Instruction *pi = ...;

for (User::op_iterator i = pi->op_begin(), e = pi->op_end(); i != e; ++i) {
  Value *v = *i;
  // ...
Iterating over predecessors & successors of blocks

Iterating over the predecessors and successors of a block is quite easy with the routines defined in "llvm/Support/CFG.h". Just use code like this to iterate over all predecessors of BB:

#include "llvm/Support/CFG.h"
BasicBlock *BB = ...;

for (pred_iterator PI = pred_begin(BB), E = pred_end(BB); PI != E; ++PI) {
  BasicBlock *Pred = *PI;
  // ...

Similarly, to iterate over successors use succ_iterator/succ_begin/succ_end.

Making simple changes

There are some primitive transformation operations present in the LLVM infrastructure that are worth knowing about. When performing transformations, it's fairly common to manipulate the contents of basic blocks. This section describes some of the common methods for doing so and gives example code.

Creating and inserting new Instructions

Instantiating Instructions

Creation of Instructions is straight-forward: simply call the constructor for the kind of instruction to instantiate and provide the necessary parameters. For example, an AllocaInst only requires a (const-ptr-to) Type. Thus:

AllocaInst* ai = new AllocaInst(Type::Int32Ty);

will create an AllocaInst instance that represents the allocation of one integer in the current stack frame, at run time. Each Instruction subclass is likely to have varying default parameters which change the semantics of the instruction, so refer to the doxygen documentation for the subclass of Instruction that you're interested in instantiating.

Naming values

It is very useful to name the values of instructions when you're able to, as this facilitates the debugging of your transformations. If you end up looking at generated LLVM machine code, you definitely want to have logical names associated with the results of instructions! By supplying a value for the Name (default) parameter of the Instruction constructor, you associate a logical name with the result of the instruction's execution at run time. For example, say that I'm writing a transformation that dynamically allocates space for an integer on the stack, and that integer is going to be used as some kind of index by some other code. To accomplish this, I place an AllocaInst at the first point in the first BasicBlock of some Function, and I'm intending to use it within the same Function. I might do:

AllocaInst* pa = new AllocaInst(Type::Int32Ty, 0, "indexLoc");

where indexLoc is now the logical name of the instruction's execution value, which is a pointer to an integer on the run time stack.

Inserting instructions

There are essentially two ways to insert an Instruction into an existing sequence of instructions that form a BasicBlock:

Deleting Instructions

Deleting an instruction from an existing sequence of instructions that form a BasicBlock is very straight-forward. First, you must have a pointer to the instruction that you wish to delete. Second, you need to obtain the pointer to that instruction's basic block. You use the pointer to the basic block to get its list of instructions and then use the erase function to remove your instruction. For example:

Instruction *I = .. ;
Replacing an Instruction with another Value

Replacing individual instructions

Including "llvm/Transforms/Utils/BasicBlockUtils.h" permits use of two very useful replace functions: ReplaceInstWithValue and ReplaceInstWithInst.

Deleting Instructions

Replacing multiple uses of Users and Values

You can use Value::replaceAllUsesWith and User::replaceUsesOfWith to change more than one use at a time. See the doxygen documentation for the Value Class and User Class, respectively, for more information.

Deleting GlobalVariables

Deleting a global variable from a module is just as easy as deleting an Instruction. First, you must have a pointer to the global variable that you wish to delete. You use this pointer to erase it from its parent, the module. For example:

GlobalVariable *GV = .. ;

Advanced Topics

This section describes some of the advanced or obscure API's that most clients do not need to be aware of. These API's tend manage the inner workings of the LLVM system, and only need to be accessed in unusual circumstances.

LLVM Type Resolution

The LLVM type system has a very simple goal: allow clients to compare types for structural equality with a simple pointer comparison (aka a shallow compare). This goal makes clients much simpler and faster, and is used throughout the LLVM system.

Unfortunately achieving this goal is not a simple matter. In particular, recursive types and late resolution of opaque types makes the situation very difficult to handle. Fortunately, for the most part, our implementation makes most clients able to be completely unaware of the nasty internal details. The primary case where clients are exposed to the inner workings of it are when building a recursive type. In addition to this case, the LLVM bitcode reader, assembly parser, and linker also have to be aware of the inner workings of this system.

For our purposes below, we need three concepts. First, an "Opaque Type" is exactly as defined in the language reference. Second an "Abstract Type" is any type which includes an opaque type as part of its type graph (for example "{ opaque, i32 }"). Third, a concrete type is a type that is not an abstract type (e.g. "{ i32, float }").

Basic Recursive Type Construction

Because the most common question is "how do I build a recursive type with LLVM", we answer it now and explain it as we go. Here we include enough to cause this to be emitted to an output .ll file:

%mylist = type { %mylist*, i32 }

To build this, use the following LLVM APIs:

// Create the initial outer struct
PATypeHolder StructTy = OpaqueType::get();
std::vector<const Type*> Elts;
StructType *NewSTy = StructType::get(Elts);

// At this point, NewSTy = "{ opaque*, i32 }". Tell VMCore that
// the struct and the opaque type are actually the same.

// NewSTy is potentially invalidated, but StructTy (a PATypeHolder) is
// kept up-to-date
NewSTy = cast<StructType>(StructTy.get());

// Add a name for the type to the module symbol table (optional)
MyModule->addTypeName("mylist", NewSTy);

This code shows the basic approach used to build recursive types: build a non-recursive type using 'opaque', then use type unification to close the cycle. The type unification step is performed by the refineAbstractTypeTo method, which is described next. After that, we describe the PATypeHolder class.

The refineAbstractTypeTo method

The refineAbstractTypeTo method starts the type unification process. While this method is actually a member of the DerivedType class, it is most often used on OpaqueType instances. Type unification is actually a recursive process. After unification, types can become structurally isomorphic to existing types, and all duplicates are deleted (to preserve pointer equality).

In the example above, the OpaqueType object is definitely deleted. Additionally, if there is an "{ \2*, i32}" type already created in the system, the pointer and struct type created are also deleted. Obviously whenever a type is deleted, any "Type*" pointers in the program are invalidated. As such, it is safest to avoid having any "Type*" pointers to abstract types live across a call to refineAbstractTypeTo (note that non-abstract types can never move or be deleted). To deal with this, the PATypeHolder class is used to maintain a stable reference to a possibly refined type, and the AbstractTypeUser class is used to update more complex datastructures.

The PATypeHolder Class

PATypeHolder is a form of a "smart pointer" for Type objects. When VMCore happily goes about nuking types that become isomorphic to existing types, it automatically updates all PATypeHolder objects to point to the new type. In the example above, this allows the code to maintain a pointer to the resultant resolved recursive type, even though the Type*'s are potentially invalidated.

PATypeHolder is an extremely light-weight object that uses a lazy union-find implementation to update pointers. For example the pointer from a Value to its Type is maintained by PATypeHolder objects.

The AbstractTypeUser Class

Some data structures need more to perform more complex updates when types get resolved. To support this, a class can derive from the AbstractTypeUser class. This class allows it to get callbacks when certain types are resolved. To register to get callbacks for a particular type, the DerivedType::{add/remove}AbstractTypeUser methods can be called on a type. Note that these methods only work for abstract types. Concrete types (those that do not include any opaque objects) can never be refined.

The ValueSymbolTable and TypeSymbolTable classes

The ValueSymbolTable class provides a symbol table that the Function and Module classes use for naming value definitions. The symbol table can provide a name for any Value. The TypeSymbolTable class is used by the Module class to store names for types.

Note that the SymbolTable class should not be directly accessed by most clients. It should only be used when iteration over the symbol table names themselves are required, which is very special purpose. Note that not all LLVM Values have names, and those without names (i.e. they have an empty name) do not exist in the symbol table.

These symbol tables support iteration over the values/types in the symbol table with begin/end/iterator and supports querying to see if a specific name is in the symbol table (with lookup). The ValueSymbolTable class exposes no public mutator methods, instead, simply call setName on a value, which will autoinsert it into the appropriate symbol table. For types, use the Module::addTypeName method to insert entries into the symbol table.

The User and owned Use classes' memory layout

The User class provides a basis for expressing the ownership of User towards other Values. The Use helper class is employed to do the bookkeeping and to facilitate O(1) addition and removal.

Interaction and relationship between User and Use objects

A subclass of User can choose between incorporating its Use objects or refer to them out-of-line by means of a pointer. A mixed variant (some Uses inline others hung off) is impractical and breaks the invariant that the Use objects belonging to the same User form a contiguous array.

We have 2 different layouts in the User (sub)classes:

As of v2.4 each layout still possesses a direct pointer to the start of the array of Uses. Though not mandatory for layout a), we stick to this redundancy for the sake of simplicity. The User object also stores the number of Use objects it has. (Theoretically this information can also be calculated given the scheme presented below.)

Special forms of allocation operators (operator new) enforce the following memory layouts:

(In the above figures 'P' stands for the Use** that is stored in each Use object in the member Use::Prev)
The waymarking algorithm

Since the Use objects are deprived of the direct (back)pointer to their User objects, there must be a fast and exact method to recover it. This is accomplished by the following scheme:

A bit-encoding in the 2 LSBits (least significant bits) of the Use::Prev allows to find the start of the User object:

Given a Use*, all we have to do is to walk till we get a stop and we either have a User immediately behind or we have to walk to the next stop picking up digits and calculating the offset:

| 1 | s | 1 | 0 | 1 | 0 | s | 1 | 1 | 0 | s | 1 | 1 | s | 1 | S | User (or User*)
    |+15                |+10            |+6         |+3     |+1
    |                   |               |           |       |__>
    |                   |               |           |__________>
    |                   |               |______________________>
    |                   |______________________________________>

Only the significant number of bits need to be stored between the stops, so that the worst case is 20 memory accesses when there are 1000 Use objects associated with a User.

Reference implementation

The following literate Haskell fragment demonstrates the concept:

> import Test.QuickCheck
> digits :: Int -> [Char] -> [Char]
> digits 0 acc = '0' : acc
> digits 1 acc = '1' : acc
> digits n acc = digits (n `div` 2) $ digits (n `mod` 2) acc
> dist :: Int -> [Char] -> [Char]
> dist 0 [] = ['S']
> dist 0 acc = acc
> dist 1 acc = let r = dist 0 acc in 's' : digits (length r) r
> dist n acc = dist (n - 1) $ dist 1 acc
> takeLast n ss = reverse $ take n $ reverse ss
> test = takeLast 40 $ dist 20 []

Printing <test> gives: "1s100000s11010s10100s1111s1010s110s11s1S"

The reverse algorithm computes the length of the string just by examining a certain prefix:

> pref :: [Char] -> Int
> pref "S" = 1
> pref ('s':'1':rest) = decode 2 1 rest
> pref (_:rest) = 1 + pref rest
> decode walk acc ('0':rest) = decode (walk + 1) (acc * 2) rest
> decode walk acc ('1':rest) = decode (walk + 1) (acc * 2 + 1) rest
> decode walk acc _ = walk + acc

Now, as expected, printing <pref test> gives 40.

We can quickCheck this with following property:

> testcase = dist 2000 []
> testcaseLength = length testcase
> identityProp n = n > 0 && n <= testcaseLength ==> length arr == pref arr
>     where arr = takeLast n testcase

As expected <quickCheck identityProp> gives:

*Main> quickCheck identityProp
OK, passed 100 tests.

Let's be a bit more exhaustive:

> deepCheck p = check (defaultConfig { configMaxTest = 500 }) p

And here is the result of <deepCheck identityProp>:

*Main> deepCheck identityProp
OK, passed 500 tests.
Tagging considerations

To maintain the invariant that the 2 LSBits of each Use** in Use never change after being set up, setters of Use::Prev must re-tag the new Use** on every modification. Accordingly getters must strip the tag bits.

For layout b) instead of the User we find a pointer (User* with LSBit set). Following this pointer brings us to the User. A portable trick ensures that the first bytes of User (if interpreted as a pointer) never has the LSBit set. (Portability is relying on the fact that all known compilers place the vptr in the first word of the instances.)

The Core LLVM Class Hierarchy Reference

#include "llvm/Type.h"
doxygen info: Type Class

The Core LLVM classes are the primary means of representing the program being inspected or transformed. The core LLVM classes are defined in header files in the include/llvm/ directory, and implemented in the lib/VMCore directory.

The Type class and Derived Types

Type is a superclass of all type classes. Every Value has a Type. Type cannot be instantiated directly but only through its subclasses. Certain primitive types (VoidType, LabelType, FloatType and DoubleType) have hidden subclasses. They are hidden because they offer no useful functionality beyond what the Type class offers except to distinguish themselves from other subclasses of Type.

All other types are subclasses of DerivedType. Types can be named, but this is not a requirement. There exists exactly one instance of a given shape at any one time. This allows type equality to be performed with address equality of the Type Instance. That is, given two Type* values, the types are identical if the pointers are identical.

Important Public Methods
Important Derived Types
Subclass of DerivedType that represents integer types of any bit width. Any bit width between IntegerType::MIN_INT_BITS (1) and IntegerType::MAX_INT_BITS (~8 million) can be represented.
  • static const IntegerType* get(unsigned NumBits): get an integer type of a specific bit width.
  • unsigned getBitWidth() const: Get the bit width of an integer type.
This is subclassed by ArrayType and PointerType
  • const Type * getElementType() const: Returns the type of each of the elements in the sequential type.
This is a subclass of SequentialType and defines the interface for array types.
  • unsigned getNumElements() const: Returns the number of elements in the array.
Subclass of SequentialType for pointer types.
Subclass of SequentialType for vector types. A vector type is similar to an ArrayType but is distinguished because it is a first class type wherease ArrayType is not. Vector types are used for vector operations and are usually small vectors of of an integer or floating point type.
Subclass of DerivedTypes for struct types.
Subclass of DerivedTypes for function types.
  • bool isVarArg() const: Returns true if its a vararg function
  • const Type * getReturnType() const: Returns the return type of the function.
  • const Type * getParamType (unsigned i): Returns the type of the ith parameter.
  • const unsigned getNumParams() const: Returns the number of formal parameters.
Sublcass of DerivedType for abstract types. This class defines no content and is used as a placeholder for some other type. Note that OpaqueType is used (temporarily) during type resolution for forward references of types. Once the referenced type is resolved, the OpaqueType is replaced with the actual type. OpaqueType can also be used for data abstraction. At link time opaque types can be resolved to actual types of the same name.
The Module class

#include "llvm/Module.h"
doxygen info: Module Class

The Module class represents the top level structure present in LLVM programs. An LLVM module is effectively either a translation unit of the original program or a combination of several translation units merged by the linker. The Module class keeps track of a list of Functions, a list of GlobalVariables, and a SymbolTable. Additionally, it contains a few helpful member functions that try to make common operations easy.

Important Public Members of the Module class

Constructing a Module is easy. You can optionally provide a name for it (probably based on the name of the translation unit).

The Value class

#include "llvm/Value.h"
doxygen info: Value Class

The Value class is the most important class in the LLVM Source base. It represents a typed value that may be used (among other things) as an operand to an instruction. There are many different types of Values, such as Constants,Arguments. Even Instructions and Functions are Values.

A particular Value may be used many times in the LLVM representation for a program. For example, an incoming argument to a function (represented with an instance of the Argument class) is "used" by every instruction in the function that references the argument. To keep track of this relationship, the Value class keeps a list of all of the Users that is using it (the User class is a base class for all nodes in the LLVM graph that can refer to Values). This use list is how LLVM represents def-use information in the program, and is accessible through the use_* methods, shown below.

Because LLVM is a typed representation, every LLVM Value is typed, and this Type is available through the getType() method. In addition, all LLVM values can be named. The "name" of the Value is a symbolic string printed in the LLVM code:

%foo = add i32 1, 2

The name of this instruction is "foo". NOTE that the name of any value may be missing (an empty string), so names should ONLY be used for debugging (making the source code easier to read, debugging printouts), they should not be used to keep track of values or map between them. For this purpose, use a std::map of pointers to the Value itself instead.

One important aspect of LLVM is that there is no distinction between an SSA variable and the operation that produces it. Because of this, any reference to the value produced by an instruction (or the value available as an incoming argument, for example) is represented as a direct pointer to the instance of the class that represents this value. Although this may take some getting used to, it simplifies the representation and makes it easier to manipulate.

Important Public Members of the Value class
The User class

#include "llvm/User.h"
doxygen info: User Class
Superclass: Value

The User class is the common base class of all LLVM nodes that may refer to Values. It exposes a list of "Operands" that are all of the Values that the User is referring to. The User class itself is a subclass of Value.

The operands of a User point directly to the LLVM Value that it refers to. Because LLVM uses Static Single Assignment (SSA) form, there can only be one definition referred to, allowing this direct connection. This connection provides the use-def information in LLVM.

Important Public Members of the User class

The User class exposes the operand list in two ways: through an index access interface and through an iterator based interface.

The Instruction class

#include "llvm/Instruction.h"
doxygen info: Instruction Class
Superclasses: User, Value

The Instruction class is the common base class for all LLVM instructions. It provides only a few methods, but is a very commonly used class. The primary data tracked by the Instruction class itself is the opcode (instruction type) and the parent BasicBlock the Instruction is embedded into. To represent a specific type of instruction, one of many subclasses of Instruction are used.

Because the Instruction class subclasses the User class, its operands can be accessed in the same way as for other Users (with the getOperand()/getNumOperands() and op_begin()/op_end() methods).

An important file for the Instruction class is the llvm/Instruction.def file. This file contains some meta-data about the various different types of instructions in LLVM. It describes the enum values that are used as opcodes (for example Instruction::Add and Instruction::ICmp), as well as the concrete sub-classes of Instruction that implement the instruction (for example BinaryOperator and CmpInst). Unfortunately, the use of macros in this file confuses doxygen, so these enum values don't show up correctly in the doxygen output.

Important Subclasses of the Instruction class
Important Public Members of the Instruction class
The Constant class and subclasses

Constant represents a base class for different types of constants. It is subclassed by ConstantInt, ConstantArray, etc. for representing the various types of Constants. GlobalValue is also a subclass, which represents the address of a global variable or function.

Important Subclasses of Constant
The GlobalValue class

#include "llvm/GlobalValue.h"
doxygen info: GlobalValue Class
Superclasses: Constant, User, Value

Global values (GlobalVariables or Functions) are the only LLVM values that are visible in the bodies of all Functions. Because they are visible at global scope, they are also subject to linking with other globals defined in different translation units. To control the linking process, GlobalValues know their linkage rules. Specifically, GlobalValues know whether they have internal or external linkage, as defined by the LinkageTypes enumeration.

If a GlobalValue has internal linkage (equivalent to being static in C), it is not visible to code outside the current translation unit, and does not participate in linking. If it has external linkage, it is visible to external code, and does participate in linking. In addition to linkage information, GlobalValues keep track of which Module they are currently part of.

Because GlobalValues are memory objects, they are always referred to by their address. As such, the Type of a global is always a pointer to its contents. It is important to remember this when using the GetElementPtrInst instruction because this pointer must be dereferenced first. For example, if you have a GlobalVariable (a subclass of GlobalValue) that is an array of 24 ints, type [24 x i32], then the GlobalVariable is a pointer to that array. Although the address of the first element of this array and the value of the GlobalVariable are the same, they have different types. The GlobalVariable's type is [24 x i32]. The first element's type is i32. Because of this, accessing a global value requires you to dereference the pointer with GetElementPtrInst first, then its elements can be accessed. This is explained in the LLVM Language Reference Manual.

Important Public Members of the GlobalValue class
The Function class

#include "llvm/Function.h"
doxygen info: Function Class
Superclasses: GlobalValue, Constant, User, Value

The Function class represents a single procedure in LLVM. It is actually one of the more complex classes in the LLVM heirarchy because it must keep track of a large amount of data. The Function class keeps track of a list of BasicBlocks, a list of formal Arguments, and a SymbolTable.

The list of BasicBlocks is the most commonly used part of Function objects. The list imposes an implicit ordering of the blocks in the function, which indicate how the code will be layed out by the backend. Additionally, the first BasicBlock is the implicit entry node for the Function. It is not legal in LLVM to explicitly branch to this initial block. There are no implicit exit nodes, and in fact there may be multiple exit nodes from a single Function. If the BasicBlock list is empty, this indicates that the Function is actually a function declaration: the actual body of the function hasn't been linked in yet.

In addition to a list of BasicBlocks, the Function class also keeps track of the list of formal Arguments that the function receives. This container manages the lifetime of the Argument nodes, just like the BasicBlock list does for the BasicBlocks.

The SymbolTable is a very rarely used LLVM feature that is only used when you have to look up a value by name. Aside from that, the SymbolTable is used internally to make sure that there are not conflicts between the names of Instructions, BasicBlocks, or Arguments in the function body.

Note that Function is a GlobalValue and therefore also a Constant. The value of the function is its address (after linking) which is guaranteed to be constant.

Important Public Members of the Function class
The GlobalVariable class

#include "llvm/GlobalVariable.h"
doxygen info: GlobalVariable Class
Superclasses: GlobalValue, Constant, User, Value

Global variables are represented with the (suprise suprise) GlobalVariable class. Like functions, GlobalVariables are also subclasses of GlobalValue, and as such are always referenced by their address (global values must live in memory, so their "name" refers to their constant address). See GlobalValue for more on this. Global variables may have an initial value (which must be a Constant), and if they have an initializer, they may be marked as "constant" themselves (indicating that their contents never change at runtime).

Important Public Members of the GlobalVariable class
The BasicBlock class

#include "llvm/BasicBlock.h"
doxygen info: BasicBlock Class
Superclass: Value

This class represents a single entry multiple exit section of the code, commonly known as a basic block by the compiler community. The BasicBlock class maintains a list of Instructions, which form the body of the block. Matching the language definition, the last element of this list of instructions is always a terminator instruction (a subclass of the TerminatorInst class).

In addition to tracking the list of instructions that make up the block, the BasicBlock class also keeps track of the Function that it is embedded into.

Note that BasicBlocks themselves are Values, because they are referenced by instructions like branches and can go in the switch tables. BasicBlocks have type label.

Important Public Members of the BasicBlock class
The Argument class

This subclass of Value defines the interface for incoming formal arguments to a function. A Function maintains a list of its formal arguments. An argument has a pointer to the parent Function.

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Last modified: $Date: 2009/03/03 05:17:36 $