LLVM Atomic Instructions and Concurrency Guide

Historically, LLVM has not had very strong support for concurrency; some minimal intrinsics were provided, and volatile was used in some cases to achieve rough semantics in the presence of concurrency. However, this is changing; there are now new instructions which are well-defined in the presence of threads and asynchronous signals, and the model for existing instructions has been clarified in the IR.

The atomic instructions are designed specifically to provide readable IR and optimized code generation for the following:

The new C++0x <atomic> header. (C++0x draft available here.) (C1x draft available here)
Proper semantics for Java-style memory, for both volatile and regular shared variables. (Java Specification)
gcc-compatible __sync_* builtins. (Description)
Other scenarios with atomic semantics, including static variables with non-trivial constructors in C++.

Atomic and volatile in the IR are orthogonal; "volatile" is the C/C++ volatile, which ensures that every volatile load and store happens and is performed in the stated order. A couple examples: if a SequentiallyConsistent store is immediately followed by another SequentiallyConsistent store to the same address, the first store can be erased. This transformation is not allowed for a pair of volatile stores. On the other hand, a non-volatile non-atomic load can be moved across a volatile load freely, but not an Acquire load.

This document is intended to provide a guide to anyone either writing a frontend for LLVM or working on optimization passes for LLVM with a guide for how to deal with instructions with special semantics in the presence of concurrency. This is not intended to be a precise guide to the semantics; the details can get extremely complicated and unreadable, and are not usually necessary.

The basic 'load' and 'store' allow a variety of optimizations, but can lead to undefined results in a concurrent environment; see NonAtomic. This section specifically goes into the one optimizer restriction which applies in concurrent environments, which gets a bit more of an extended description because any optimization dealing with stores needs to be aware of it.

From the optimizer's point of view, the rule is that if there are not any instructions with atomic ordering involved, concurrency does not matter, with one exception: if a variable might be visible to another thread or signal handler, a store cannot be inserted along a path where it might not execute otherwise. Take the following example:

/* C code, for readability; run through clang -O2 -S -emit-llvm to get
   equivalent IR */
int x;
void f(int* a) {
  for (int i = 0; i < 100; i++) {
    if (a[i])
      x += 1;
  }
}

The following is equivalent in non-concurrent situations:

int x;
void f(int* a) {
  int xtemp = x;
  for (int i = 0; i < 100; i++) {
    if (a[i])
      xtemp += 1;
  }
  x = xtemp;
}

However, LLVM is not allowed to transform the former to the latter: it could introduce undefined behavior if another thread can access x at the same time. (This example is particularly of interest because before the concurrency model was implemented, LLVM would perform this transformation.)

Note that speculative loads are allowed; a load which is part of a race returns undef, but does not have undefined behavior.

For cases where simple loads and stores are not sufficient, LLVM provides various atomic instructions. The exact guarantees provided depend on the ordering; see Atomic orderings

load atomic and store atomic provide the same basic functionality as non-atomic loads and stores, but provide additional guarantees in situations where threads and signals are involved.

cmpxchg and atomicrmw are essentially like an atomic load followed by an atomic store (where the store is conditional for cmpxchg), but no other memory operation can happen on any thread between the load and store. Note that LLVM's cmpxchg does not provide quite as many options as the C++0x version.

A fence provides Acquire and/or Release ordering which is not part of another operation; it is normally used along with Monotonic memory operations. A Monotonic load followed by an Acquire fence is roughly equivalent to an Acquire load.

Frontends generating atomic instructions generally need to be aware of the target to some degree; atomic instructions are guaranteed to be lock-free, and therefore an instruction which is wider than the target natively supports can be impossible to generate.

In order to achieve a balance between performance and necessary guarantees, there are six levels of atomicity. They are listed in order of strength; each level includes all the guarantees of the previous level except for Acquire/Release.

NotAtomic

NotAtomic is the obvious, a load or store which is not atomic. (This isn't really a level of atomicity, but is listed here for comparison.) This is essentially a regular load or store. If code accesses a memory location from multiple threads at the same time, the resulting loads return 'undef'.

Relevant standard: This is intended to match shared variables in C/C++, and to be used in any other context where memory access is necessary, and a race is impossible.
Notes for frontends: The rule is essentially that all memory accessed with basic loads and stores by multiple threads should be protected by a lock or other synchronization; otherwise, you are likely to run into undefined behavior. If your frontend is for a "safe" language like Java, use Unordered to load and store any shared variable. Note that NotAtomic volatile loads and stores are not properly atomic; do not try to use them as a substitute. (Per the C/C++ standards, volatile does provide some limited guarantees around asynchronous signals, but atomics are generally a better solution.)
Notes for optimizers: Introducing loads to shared variables along a codepath where they would not otherwise exist is allowed; introducing stores to shared variables is not. See Optimization outside atomic.
Notes for code generation: The one interesting restriction here is that it is not allowed to write to bytes outside of the bytes relevant to a store. This is mostly relevant to unaligned stores: it is not allowed in general to convert an unaligned store into two aligned stores of the same width as the unaligned store. Backends are also expected to generate an i8 store as an i8 store, and not an instruction which writes to surrounding bytes. (If you are writing a backend for an architecture which cannot satisfy these restrictions and cares about concurrency, please send an email to llvmdev.)

Unordered

Unordered is the lowest level of atomicity. It essentially guarantees that races produce somewhat sane results instead of having undefined behavior. It also guarantees the operation to be lock-free, so it do not depend on the data being part of a special atomic structure or depend on a separate per-process global lock. Note that code generation will fail for unsupported atomic operations; if you need such an operation, use explicit locking.

Relevant standard: This is intended to match the Java memory model for shared variables.
Notes for frontends: This cannot be used for synchronization, but is useful for Java and other "safe" languages which need to guarantee that the generated code never exhibits undefined behavior. Note that this guarantee is cheap on common platforms for loads of a native width, but can be expensive or unavailable for wider loads, like a 64-bit store on ARM. (A frontend for Java or other "safe" languages would normally split a 64-bit store on ARM into two 32-bit unordered stores.)
Notes for optimizers: In terms of the optimizer, this prohibits any transformation that transforms a single load into multiple loads, transforms a store into multiple stores, narrows a store, or stores a value which would not be stored otherwise. Some examples of unsafe optimizations are narrowing an assignment into a bitfield, rematerializing a load, and turning loads and stores into a memcpy call. Reordering unordered operations is safe, though, and optimizers should take advantage of that because unordered operations are common in languages that need them.
Notes for code generation: These operations are required to be atomic in the sense that if you use unordered loads and unordered stores, a load cannot see a value which was never stored. A normal load or store instruction is usually sufficient, but note that an unordered load or store cannot be split into multiple instructions (or an instruction which does multiple memory operations, like LDRD on ARM).

Monotonic

Monotonic is the weakest level of atomicity that can be used in synchronization primitives, although it does not provide any general synchronization. It essentially guarantees that if you take all the operations affecting a specific address, a consistent ordering exists.

Relevant standard: This corresponds to the C++0x/C1x memory_order_relaxed; see those standards for the exact definition.
Notes for frontends: If you are writing a frontend which uses this directly, use with caution. The guarantees in terms of synchronization are very weak, so make sure these are only used in a pattern which you know is correct. Generally, these would either be used for atomic operations which do not protect other memory (like an atomic counter), or along with a fence.
Notes for optimizers: In terms of the optimizer, this can be treated as a read+write on the relevant memory location (and alias analysis will take advantage of that). In addition, it is legal to reorder non-atomic and Unordered loads around Monotonic loads. CSE/DSE and a few other optimizations are allowed, but Monotonic operations are unlikely to be used in ways which would make those optimizations useful.
Notes for code generation: Code generation is essentially the same as that for unordered for loads and stores. No fences is required. cmpxchg and atomicrmw are required to appear as a single operation.

Acquire

Acquire provides a barrier of the sort necessary to acquire a lock to access other memory with normal loads and stores.

Relevant standard: This corresponds to the C++0x/C1x memory_order_acquire. It should also be used for C++0x/C1x memory_order_consume.
Notes for frontends: If you are writing a frontend which uses this directly, use with caution. Acquire only provides a semantic guarantee when paired with a Release operation.
Notes for optimizers: Optimizers not aware of atomics can treat this like a nothrow call. It is also possible to move stores from before an Acquire load or read-modify-write operation to after it, and move non-Acquire loads from before an Acquire operation to after it.
Notes for code generation: Architectures with weak memory ordering (essentially everything relevant today except x86 and SPARC) require some sort of fence to maintain the Acquire semantics. The precise fences required varies widely by architecture, but for a simple implementation, most architectures provide a barrier which is strong enough for everything (dmb on ARM, sync on PowerPC, etc.). Putting such a fence after the equivalent Monotonic operation is sufficient to maintain Acquire semantics for a memory operation.

Release

Release is similar to Acquire, but with a barrier of the sort necessary to release a lock.

Relevant standard: This corresponds to the C++0x/C1x memory_order_release.
Notes for frontends: If you are writing a frontend which uses this directly, use with caution. Release only provides a semantic guarantee when paired with a Acquire operation.
Notes for optimizers: Optimizers not aware of atomics can treat this like a nothrow call. It is also possible to move loads from after a Release store or read-modify-write operation to before it, and move non-Release stores from after an Release operation to before it.
Notes for code generation: See the section on Acquire; a fence before the relevant operation is usually sufficient for Release. Note that a store-store fence is not sufficient to implement Release semantics; store-store fences are generally not exposed to IR because they are extremely difficult to use correctly.

AcquireRelease

AcquireRelease (acq_rel in IR) provides both an Acquire and a Release barrier (for fences and operations which both read and write memory).

Relevant standard: This corresponds to the C++0x/C1x memory_order_acq_rel.
Notes for frontends: If you are writing a frontend which uses this directly, use with caution. Acquire only provides a semantic guarantee when paired with a Release operation, and vice versa.
Notes for optimizers: In general, optimizers should treat this like a nothrow call; the the possible optimizations are usually not interesting.
Notes for code generation: This operation has Acquire and Release semantics; see the sections on Acquire and Release.

SequentiallyConsistent

SequentiallyConsistent (seq_cst in IR) provides Acquire semantics for loads and Release semantics for stores. Additionally, it guarantees that a total ordering exists between all SequentiallyConsistent operations.

Relevant standard: This corresponds to the C++0x/C1x memory_order_seq_cst, Java volatile, and the gcc-compatible __sync_* builtins which do not specify otherwise.
Notes for frontends: If a frontend is exposing atomic operations, these are much easier to reason about for the programmer than other kinds of operations, and using them is generally a practical performance tradeoff.
Notes for optimizers: Optimizers not aware of atomics can treat this like a nothrow call. For SequentiallyConsistent loads and stores, the same reorderings are allowed as for Acquire loads and Release stores, except that SequentiallyConsistent operations may not be reordered.
Notes for code generation: SequentiallyConsistent loads minimally require the same barriers as Acquire operations and SequeuentiallyConsistent stores require Release barriers. Additionally, the code generator must enforce ordering between SequeuentiallyConsistent stores followed by SequeuentiallyConsistent loads. This is usually done by emitting either a full fence before the loads or a full fence after the stores; which is preferred varies by architecture.

Predicates for optimizer writers to query:

isSimple(): A load or store which is not volatile or atomic. This is what, for example, memcpyopt would check for operations it might transform.
isUnordered(): A load or store which is not volatile and at most Unordered. This would be checked, for example, by LICM before hoisting an operation.
mayReadFromMemory()/mayWriteToMemory(): Existing predicate, but note that they return true for any operation which is volatile or at least Monotonic.
Alias analysis: Note that AA will return ModRef for anything Acquire or Release, and for the address accessed by any Monotonic operation.

To support optimizing around atomic operations, make sure you are using the right predicates; everything should work if that is done. If your pass should optimize some atomic operations (Unordered operations in particular), make sure it doesn't replace an atomic load or store with a non-atomic operation.

Some examples of how optimizations interact with various kinds of atomic operations:

memcpyopt: An atomic operation cannot be optimized into part of a memcpy/memset, including unordered loads/stores. It can pull operations across some atomic operations.
LICM: Unordered loads/stores can be moved out of a loop. It just treats monotonic operations like a read+write to a memory location, and anything stricter than that like a nothrow call.
DSE: Unordered stores can be DSE'ed like normal stores. Monotonic stores can be DSE'ed in some cases, but it's tricky to reason about, and not especially important.
Folding a load: Any atomic load from a constant global can be constant-folded, because it cannot be observed. Similar reasoning allows scalarrepl with atomic loads and stores.

Atomic operations are represented in the SelectionDAG with ATOMIC_* opcodes. On architectures which use barrier instructions for all atomic ordering (like ARM), appropriate fences are split out as the DAG is built.

The MachineMemOperand for all atomic operations is currently marked as volatile; this is not correct in the IR sense of volatile, but CodeGen handles anything marked volatile very conservatively. This should get fixed at some point.

Common architectures have some way of representing at least a pointer-sized lock-free cmpxchg; such an operation can be used to implement all the other atomic operations which can be represented in IR up to that size. Backends are expected to implement all those operations, but not operations which cannot be implemented in a lock-free manner. It is expected that backends will give an error when given an operation which cannot be implemented. (The LLVM code generator is not very helpful here at the moment, but hopefully that will change.)

The implementation of atomics on LL/SC architectures (like ARM) is currently a bit of a mess; there is a lot of copy-pasted code across targets, and the representation is relatively unsuited to optimization (it would be nice to be able to optimize loops involving cmpxchg etc.).

On x86, all atomic loads generate a MOV. SequentiallyConsistent stores generate an XCHG, other stores generate a MOV. SequentiallyConsistent fences generate an MFENCE, other fences do not cause any code to be generated. cmpxchg uses the LOCK CMPXCHG instruction. atomicrmw xchg uses XCHG, atomicrmw add and atomicrmw sub use XADD, and all other atomicrmw operations generate a loop with LOCK CMPXCHG. Depending on the users of the result, some atomicrmw operations can be translated into operations like LOCK AND, but that does not work in general.

On ARM, MIPS, and many other RISC architectures, Acquire, Release, and SequentiallyConsistent semantics require barrier instructions for every such operation. Loads and stores generate normal instructions. cmpxchg and atomicrmw can be represented using a loop with LL/SC-style instructions which take some sort of exclusive lock on a cache line (LDREX and STREX on ARM, etc.). At the moment, the IR does not provide any way to represent a weak cmpxchg which would not require a loop.

LLVM Atomic Instructions and Concurrency Guide

Introduction

Optimization outside atomic

Atomic instructions

Atomic orderings