Alias Analysis (aka Pointer Analysis) is a class of techniques which attempt to determine whether or not two pointers ever can point to the same object in memory. There are many different algorithms for alias analysis and many different ways of classifying them: flow-sensitive vs flow-insensitive, context-sensitive vs context-insensitive, field-sensitive vs field-insensitive, unification-based vs subset-based, etc. Traditionally, alias analyses respond to a query with a Must, May, or No alias response, indicating that two pointers always point to the same object, might point to the same object, or are known to never point to the same object.

The LLVM AliasAnalysis class is the primary interface used by clients and implementations of alias analyses in the LLVM system. This class is the common interface between clients of alias analysis information and the implementations providing it, and is designed to support a wide range of implementations and clients (but currently all clients are assumed to be flow-insensitive). In addition to simple alias analysis information, this class exposes Mod/Ref information from those implementations which can provide it, allowing for powerful analyses and transformations to work well together.

This document contains information necessary to successfully implement this interface, use it, and to test both sides. It also explains some of the finer points about what exactly results mean. If you feel that something is unclear or should be added, please let me know.

The AliasAnalysis class defines the interface that the various alias analysis implementations should support. This class exports two important enums: AliasResult and ModRefResult which represent the result of an alias query or a mod/ref query, respectively.

The AliasAnalysis interface exposes information about memory, represented in several different ways. In particular, memory objects are represented as a starting address and size, and function calls are represented as the actual call or invoke instructions that performs the call. The AliasAnalysis interface also exposes some helper methods which allow you to get mod/ref information for arbitrary instructions.

Most importantly, the AliasAnalysis class provides several methods which are used to query whether or not two memory objects alias, whether function calls can modify or read a memory object, etc. For all of these queries, memory objects are represented as a pair of their starting address (a symbolic LLVM Value*) and a static size.

Representing memory objects as a starting address and a size is critically important for correct Alias Analyses. For example, consider this (silly, but possible) C code:

  int i;
  char C[2];
  char A[10]; 
  /* ... */
  for (i = 0; i != 10; ++i) {
    C[0] = A[i];          /* One byte store */
    C[1] = A[9-i];        /* One byte store */
  }

In this case, the basicaa pass will disambiguate the stores to C[0] and C[1] because they are accesses to two distinct locations one byte apart, and the accesses are each one byte. In this case, the LICM pass can use store motion to remove the stores from the loop. In constrast, the following code:

  int i;
  char C[2];
  char A[10]; 
  /* ... */
  for (i = 0; i != 10; ++i) {
    ((short*)C)[0] = A[i];  /* Two byte store! */
    C[1] = A[9-i];          /* One byte store */
  }

In this case, the two stores to C do alias each other, because the access to the &C[0] element is a two byte access. If size information wasn't available in the query, even the first case would have to conservatively assume that the accesses alias.

The alias method is the primary interface used to determine whether or not two memory objects alias each other. It takes two memory objects as input and returns MustAlias, MayAlias, or NoAlias as appropriate.

An Alias Analysis implementation can return one of three responses: MustAlias, MayAlias, and NoAlias. The No and May alias results are obvious: if the two pointers can never equal each other, return NoAlias, if they might, return MayAlias.

The MustAlias response is trickier though. In LLVM, the Must Alias response may only be returned if the two memory objects are guaranteed to always start at exactly the same location. If two memory objects overlap, but do not start at the same location, return MayAlias.

The getModRefInfo methods return information about whether the execution of an instruction can read or modify a memory location. Mod/Ref information is always conservative: if an instruction might read or write a location, ModRef is returned.

The AliasAnalysis class also provides a getModRefInfo method for testing dependencies between function calls. This method takes two call sites (CS1 & CS2), returns NoModRef if the two calls refer to disjoint memory locations, Ref if CS1 reads memory written by CS2, Mod if CS1 writes to memory read or written by CS2, or ModRef if CS1 might read or write memory accessed by CS2. Note that this relation is not commutative. Clients that use this method should be predicated on the hasNoModRefInfoForCalls() method, which indicates whether or not an analysis can provide mod/ref information for function call pairs (most can not). If this predicate is false, the client shouldn't waste analysis time querying the getModRefInfo method many times.

Several other tidbits of information are often collected by various alias analysis implementations and can be put to good use by various clients.

The getMustAliases method returns all values that are known to always must alias a pointer. This information can be provided in some cases for important objects like the null pointer and global values. Knowing that a pointer always points to a particular function allows indirect calls to be turned into direct calls, for example.

The pointsToConstantMemory method returns true if and only if the analysis can prove that the pointer only points to unchanging memory locations (functions, constant global variables, and the null pointer). This information can be used to refine mod/ref information: it is impossible for an unchanging memory location to be modified.

These methods are used to provide very simple mod/ref information for function calls. The doesNotAccessMemory method returns true for a function if the analysis can prove that the function never reads or writes to memory, or if the function only reads from constant memory. Functions with this property are side-effect free and only depend on their input arguments, allowing them to be eliminated if they form common subexpressions or be hoisted out of loops. Many common functions behave this way (e.g., sin and cos) but many others do not (e.g., acos, which modifies the errno variable).

The onlyReadsMemory method returns true for a function if analysis can prove that (at most) the function only reads from non-volatile memory. Functions with this property are side-effect free, only depending on their input arguments and the state of memory when they are called. This property allows calls to these functions to be eliminated and moved around, as long as there is no store instruction that changes the contents of memory. Note that all functions that satisfy the doesNotAccessMemory method also satisfies onlyReadsMemory.

Writing a new alias analysis implementation for LLVM is quite straight-forward. There are already several implementations that you can use for examples, and the following information should help fill in any details. For a examples, take a look at the various alias analysis implementations included with LLVM.

The first step to determining what type of LLVM pass you need to use for your Alias Analysis. As is the case with most other analyses and transformations, the answer should be fairly obvious from what type of problem you are trying to solve:

1. If you require interprocedural analysis, it should be a Pass.
2. If you are a function-local analysis, subclass FunctionPass.
3. If you don't need to look at the program at all, subclass ImmutablePass.

In addition to the pass that you subclass, you should also inherit from the AliasAnalysis interface, of course, and use the RegisterAnalysisGroup template to register as an implementation of AliasAnalysis.

Your subclass of AliasAnalysis is required to invoke two methods on the AliasAnalysis base class: getAnalysisUsage and InitializeAliasAnalysis. In particular, your implementation of getAnalysisUsage should explicitly call into the AliasAnalysis::getAnalysisUsage method in addition to doing any declaring any pass dependencies your pass has. Thus you should have something like this:

    void getAnalysisUsage(AnalysisUsage &AU) const {
      AliasAnalysis::getAnalysisUsage(AU);
      // declare your dependencies here.
    }

Additionally, your must invoke the InitializeAliasAnalysis method from your analysis run method (run for a Pass, runOnFunction for a FunctionPass, or InitializePass for an ImmutablePass). For example (as part of a Pass):

    bool run(Module &M) {
      InitializeAliasAnalysis(this);
      // Perform analysis here...
      return false;
    }

All of the AliasAnalysis virtual methods default to providing chaining to another alias analysis implementation, which ends up returning conservatively correct information (returning "May" Alias and "Mod/Ref" for alias and mod/ref queries respectively). Depending on the capabilities of the analysis you are implementing, you just override the interfaces you can improve.

With only two special exceptions (the basicaa and no-aa passes) every alias analysis pass chains to another alias analysis implementation (for example, the user can specify "-basicaa -ds-aa -anders-aa -licm" to get the maximum benefit from the three alias analyses). The alias analysis class automatically takes care of most of this for methods that you don't override. For methods that you do override, in code paths that return a conservative MayAlias or Mod/Ref result, simply return whatever the superclass computes. For example:

  AliasAnalysis::AliasResult alias(const Value *V1, unsigned V1Size,
                                   const Value *V2, unsigned V2Size) {
    if (...)
      return NoAlias;
    ...

    // Couldn't determine a must or no-alias result.
    return AliasAnalysis::alias(V1, V1Size, V2, V2Size);
  }

In addition to analysis queries, you must make sure to unconditionally pass LLVM update notification methods to the superclass as well if you override them, which allows all alias analyses in a change to be updated.

Alias analysis information is initially computed for a static snapshot of the program, but clients will use this information to make transformations to the code. All but the most trivial forms of alias analysis will need to have their analysis results updated to reflect the changes made by these transformations.

The AliasAnalysis interface exposes two methods which are used to communicate program changes from the clients to the analysis implementations. Various alias analysis implementations should use these methods to ensure that their internal data structures are kept up-to-date as the program changes (for example, when an instruction is deleted), and clients of alias analysis must be sure to call these interfaces appropriately.

The deleteValue method is called by transformations when they remove an instruction or any other value from the program (including values that do not use pointers). Typically alias analyses keep data structures that have entries for each value in the program. When this method is called, they should remove any entries for the specified value, if they exist.

The copyValue method is used when a new value is introduced into the program. There is no way to introduce a value into the program that did not exist before (this doesn't make sense for a safe compiler transformation), so this is the only way to introduce a new value. This method indicates that the new value has exactly the same properties as the value being copied.

This method is a simple helper method that is provided to make clients easier to use. It is implemented by copying the old analysis information to the new value, then deleting the old value. This method cannot be overridden by alias analysis implementations.

From the LLVM perspective, the only thing you need to do to provide an efficient alias analysis is to make sure that alias analysis queries are serviced quickly. The actual calculation of the alias analysis results (the "run" method) is only performed once, but many (perhaps duplicate) queries may be performed. Because of this, try to move as much computation to the run method as possible (within reason).

There are several different ways to use alias analysis results. In order of preference, these are...

The load-vn pass uses alias analysis to provide value numbering information for load instructions and pointer values. If your analysis or transformation can be modeled in a form that uses value numbering information, you don't have to do anything special to handle load instructions: just use the load-vn pass, which uses alias analysis.

Many transformations need information about alias sets that are active in some scope, rather than information about pairwise aliasing. The AliasSetTracker class is used to efficiently build these Alias Sets from the pairwise alias analysis information provided by the AliasAnalysis interface.

First you initialize the AliasSetTracker by using the "add" methods to add information about various potentially aliasing instructions in the scope you are interested in. Once all of the alias sets are completed, your pass should simply iterate through the constructed alias sets, using the AliasSetTracker begin()/end() methods.

The AliasSets formed by the AliasSetTracker are guaranteed to be disjoint, calculate mod/ref information and volatility for the set, and keep track of whether or not all of the pointers in the set are Must aliases. The AliasSetTracker also makes sure that sets are properly folded due to call instructions, and can provide a list of pointers in each set.

As an example user of this, the Loop Invariant Code Motion pass uses AliasSetTrackers to calculate alias sets for each loop nest. If an AliasSet in a loop is not modified, then all load instructions from that set may be hoisted out of the loop. If any alias sets are stored to and are must alias sets, then the stores may be sunk to outside of the loop, promoting the memory location to a register for the duration of the loop nest. Both of these transformations only apply if the pointer argument is loop-invariant.

The AliasSetTracker class is implemented to be as efficient as possible. It uses the union-find algorithm to efficiently merge AliasSets when a pointer is inserted into the AliasSetTracker that aliases multiple sets. The primary data structure is a hash table mapping pointers to the AliasSet they are in.

The AliasSetTracker class must maintain a list of all of the LLVM Value*'s that are in each AliasSet. Since the hash table already has entries for each LLVM Value* of interest, the AliasesSets thread the linked list through these hash-table nodes to avoid having to allocate memory unnecessarily, and to make merging alias sets extremely efficient (the linked list merge is constant time).

You shouldn't need to understand these details if you are just a client of the AliasSetTracker, but if you look at the code, hopefully this brief description will help make sense of why things are designed the way they are.

If neither of these utility class are what your pass needs, you should use the interfaces exposed by the AliasAnalysis class directly. Try to use the higher-level methods when possible (e.g., use mod/ref information instead of the alias method directly if possible) to get the best precision and efficiency.

If you're going to be working with the LLVM alias analysis infrastructure, you should know what clients and implementations of alias analysis are available. In particular, if you are implementing an alias analysis, you should be aware of the the clients that are useful for monitoring and evaluating different implementations.

This section lists the various implementations of the AliasAnalysis interface. With the exception of the -no-aa and -basicaa implementations, all of these chain to other alias analysis implementations.

The -no-aa pass is just like what it sounds: an alias analysis that never returns any useful information. This pass can be useful if you think that alias analysis is doing something wrong and are trying to narrow down a problem.

The -basicaa pass is the default LLVM alias analysis. It is an aggressive local analysis that "knows" many important facts:

• Distinct globals, stack allocations, and heap allocations can never alias.
• Globals, stack allocations, and heap allocations never alias the null pointer.
• Different fields of a structure do not alias.
• Indexes into arrays with statically differing subscripts cannot alias.
• Many common standard C library functions never access memory or only read memory.
• Pointers that obviously point to constant globals "pointToConstantMemory".
• Function calls can not modify or references stack allocations if they never escape from the function that allocates them (a common case for automatic arrays).

This pass implements a simple context-sensitive mod/ref and alias analysis for internal global variables that don't "have their address taken". If a global does not have its address taken, the pass knows that no pointers alias the global.

The real power of this pass is that it provides context-sensitive mod/ref information for call instructions. This allows the optimizer to know that calls to a function do not clobber or read the value of the global, allowing loads and stores to be eliminated.

Note that this pass is somewhat limited in its scope (only support non-address taken globals), but is very quick analysis.

The -anders-aa pass implements the well-known "Andersen's algorithm" for interprocedural alias analysis. This algorithm is a subset-based, flow-insensitive, context-insensitive, and field-insensitive alias analysis that is widely believed to be fairly precise. Unfortunately, this algorithm is also O(N³). The LLVM implementation currently does not implement any of the refinements (such as "online cycle elimination" or "offline variable substitution") to improve its efficiency, so it can be quite slow in common cases.

The -steens-aa pass implements a variation on the well-known "Steensgaard's algorithm" for interprocedural alias analysis. Steensgaard's algorithm is a unification-based, flow-insensitive, context-insensitive, and field-insensitive alias analysis that is also very scalable (effectively linear time).

The LLVM -steens-aa pass implements a "speculatively field-sensitive" version of Steensgaard's algorithm using the Data Structure Analysis framework. This gives it substantially more precision than the standard algorithm while maintaining excellent analysis scalability.

The -ds-aa pass implements the full Data Structure Analysis algorithm. Data Structure Analysis is a modular unification-based, flow-insensitive, context-sensitive, and speculatively field-sensitive alias analysis that is also quite scalable, usually at O(n*log(n)).

This algorithm is capable of responding to a full variety of alias analysis queries, and can provide context-sensitive mod/ref information as well. The only major facility not implemented so far is support for must-alias information.

LLVM includes several alias-analysis driven transformations which can be used with any of the implementations above.

The -adce pass, which implements Aggressive Dead Code Elimination uses the AliasAnalysis interface to delete calls to functions that do not have side-effects and are not used.

The -licm pass implements various Loop Invariant Code Motion related transformations. It uses the AliasAnalysis interface for several different transformations:

• It uses mod/ref information to hoist or sink load instructions out of loops if there are no instructions in the loop that modifies the memory loaded.
• It uses mod/ref information to hoist function calls out of loops that do not write to memory and are loop-invariant.
• If uses alias information to promote memory objects that are loaded and stored to in loops to live in a register instead. It can do this if there are no may aliases to the loaded/stored memory location.

The -argpromotion pass promotes by-reference arguments to be passed in by-value instead. In particular, if pointer arguments are only loaded from it passes in the value loaded instead of the address to the function. This pass uses alias information to make sure that the value loaded from the argument pointer is not modified between the entry of the function and any load of the pointer.

The -load-vn pass uses alias analysis to "value number" loads and pointers values, which is used by the GCSE pass to eliminate instructions. The -load-vn pass relies on alias information and must-alias information. This combination of passes can make the following transformations:

• Redundant load instructions are eliminated.
• Load instructions that follow a store to the same location are replaced with the stored value ("store forwarding").
• Pointers values (e.g. formal arguments) that must-alias simpler expressions (e.g. global variables or the null pointer) are replaced. Note that this implements transformations like "virtual method resolution", turning indirect calls into direct calls.

The -print-alias-sets pass is exposed as part of the analyze tool to print out the Alias Sets formed by the AliasSetTracker class. This is useful if you're using the AliasSetTracker class.

The -count-aa pass is useful to see how many queries a particular pass is making and what responses are returned by the alias analysis. An example usage is:

  $ opt -basicaa -count-aa -ds-aa -count-aa -licm

Which will print out how many queries (and what responses are returned) by the -licm pass (of the -ds-aa pass) and how many queries are made of the -basicaa pass by the -ds-aa pass. This can be useful when debugging a transformation or an alias analysis implementation.

The -aa-eval pass simply iterates through all pairs of pointers in a function and asks an alias analysis whether or not the pointers alias. This gives an indication of the precision of the alias analysis. Statistics are printed indicating the percent of no/may/must aliases found (a more precise algorithm will have a lower number of may aliases).