Using JDK 9 Memory Order Modes

by Doug Lea.

Last update: Fri Mar 17 08:12:37 2017


This guide is mainly intended for expert programmers familiar with Java concurrency, but unfamiliar with the memory order modes available in JDK 9 provided by VarHandles. Mostly, it focuses on how to think about modes when developing parallel software. Feel free to first read the Summary. (Also feel free to adjust the display format of this document using for example Firefox Reader View.)

To get the shockingly ugly syntactic details over with: A VarHandle can be associated with any field, array element, or static, allowing control over access modes. VarHandles should be declared as static final fields and explicitly initialized in static blocks. By convention, we give VarHandles for fields names that are uppercase versions of the field names. For example, in a Point class:

import java.lang.invoke.MethodHandles;
import java.lang.invoke.VarHandle;
class Point {
   volatile int x, y;
   private static final VarHandle X;
   static {
     try {
       X = MethodHandles.lookup().
           findVarHandle(Point.class, "x",
     } catch (ReflectiveOperationException e) {
       throw new Error(e);
   // ...
Within some Point method, field x can be read, for example in Acquire mode using int v = X.getAcquire(this). For more details, see the API documentation and JEP 193. As a matter of good practice, all fields intended to be accessed concurrently should be declared as volatile, which provides the least surprising defaults when they are accessed directly without VarHandles. This cannot be expressed when using VarHandles with array elements, so the array declarations should be manually documented that they support concurrent access.

Also, JDK 9 versions of java.util.concurrent.atomic classes include methods corresponding to these VarHandle constructions, applied to the single elements or arrays held by the associated Atomic objects.

A planned follow-up will present more detailed examples of VarHandle usages and further coding guidelines.


Back in the earliest days of concurrent programming (predating Java), computers were much simpler devices. Uniprocessors single-stepped through instructions accessing memory cells, and emulated concurrency by context-switching across threads. While many of the pioneering ideas about coordination and interference in concurrent programming established during this era still hold, others turn out to be ill-matched for systems employing three forms of parallelism that have since emerged:

  1. Task parallelism. Under uniprocessor emulation, if two threads execute basic actions A and B respectively, then either A precedes B or B precedes A. But with multiple cores, A and B may be unordered -- neither precedes the other.
  2. Memory parallelism. When memory is managed by multiple parallel agents (especially including caches), then variables need not be directly represented by any single physical device. So the notion of a variable is a matter of agreement among threads about values associated with an address.
  3. Instruction parallelism. Rather than single-stepping, CPUs process instructions in an overlapped fashion, so multiple instructions can be in-process at the same time.

Concepts and techniques for dealing with these forms of parallelism are maturing to the point that the same ideas regularly appear across different programming languages, processor architectures, and even non-shared memory (distributed) systems. This guide focuses on Java, but also includes some brief remarks about other languages, notes on processor-level issues that are abstracted over at the language level, and also a few ties to distributed (cluster and cloud) consistency models that otherwise differ mainly with respect to fault tolerance issues that don't normally arise in shared memory systems.

Across these, no single rule or model makes sense for all code in all programs. So there must be multiple models, or modes, along with accounts of how they interrelate. This was seen even in the early days of concurrency. The idea of a Monitor introduced in the 1960s implicitly established two modes, leading to rules for "normal" code appearing in lock-protected bodies, and other rules for accessing and ordering locks. In the 1990s, Java introduced another mode, volatile, that wasn't formally well-specified until JSR133 in 2004.

Experience with multicores has shown that a few more modes are needed to deal with common concurrent programming problems. Without them, some programmers over-synchronize code, which can make programs slow, some programmers under-synchronize code, which can make programs wrong, and other programmers work around limitations by using unstandardized operations available on particular JVMs and processors, which can make programs unportable.

The new memory order modes are defined with cumulative effect, from weakest to strongest: Plain, Opaque, Release/Acquire, and Volatile. The existing Plain and Volatile modes are defined compatibly with their pre-JDK 9 forms. Any guaranteed property of a weaker mode, plus more, holds for a stronger mode. (Conversely, implementations are allowed to use a stronger mode than requested for any access.) In JDK 9, these are provided without a full formal specification. This document does not include a specification of memory order modes. It instead discusses usage in terms of their properties (mainly: commutativity, coherence, causality, and consensus) that build upon one another. The resulting memory consistency rules can be thought of in terms of increasingly restrictive caching protocols.

Because stronger modes impose more ordering constraints, they reduce potential parallelism (in at least one of the above senses) but may provide guarantees that programs rely on. When you don't enforce required constraints, they may hold anyway sometimes but not always, resulting in hard-to-replicate software errors.

When the strongest synchronization and ordering constructions such as monitors were first devised, the resulting constraints on parallelism were not much of an issue because there was not much parallelism available anyway. But now, choices often entail engineering tradeoffs. Enabling parallelism generally improves scalability. In the fastest but least controlled parallel programs, every thread accesses only local variables and encounters no ordering or resource constraints. But other cases may encounter time, space, energy, and complexity issues that don't always result in better performance on any given program or platform.

The use of multiple consistency modes can also make correctness more difficult to establish. The existence of a "data race" is not always a yes/no matter, but is the result of using a weaker mode than you needed to preserve correctness. Further, many concurrency bugs have little to do with modes per se. For example, no choice of mode will fix check-then-act errors of the form if (v != null) use(v), where v is a concurrently updated variable that may become null between the check and use. Existing concurrency tools do not make these fine distinctions, so analysis and testing may be more difficult. Additionally, the details of using weaker modes, especially in lock-free programming, may clash with established lock-oriented rules and conventions, so some care may be required when choosing among equivalent-seeming ways of expressing constraints. But if you are reading this, then you are probably interested in exploring the tradeoffs encountered when arranging for and exploiting parallelism.

Ordering Relations

Memory order modes describe relations among memory accesses (reads, writes, and atomic updates), and only incidentally constrain other computations. These relations are defined over accesses as Read, Write, and Update events, not the values accessed. The relations focus on ordering because they restrict the potential parallelism that would be enabled by the lack of ordering constraints. Access events might have observable durations, but are constrained in terms of instantaneous "commit" points.

Here's some terminology about ordering relations as applied to events: Strict irreflexive orders act like less-than, not less-than-or-equals. In a (strict) total order each event is ordered with respect to every other event, resulting in a linear (sequential) chain of events. In a (strict) partial order, any two events need not be related at all (so might be concurrent) but there are no cycles (circularities). A linear extension (also known as a topological sort) of a partial order is one of possibly many sequentializations (total orderings) that obey all of its ordering constraints. In a partial order, two events might be unordered, but in any linear extension, one precedes the other.

Most memory ordering properties are ultimately based on only two (strict) relations, one within threads, and one across threads. As described below, intrathread local precedence denotes that access A precedes access B within the same thread. The Read-by relation orders interthread accesses: For variable x, if Read Rx reads from the write of Write Wx, then Wx must occur before Rx. The corresponding reads-from function from each Read to its source Write has the opposite directionality, and can be simpler to use in specifications.

These two relations can be used together to define the antecedence relation that links temporal orderings within threads with those across threads. This takes the same form as the original happens-before relation defined by Leslie Lamport in 1978. But because happens-before has also been defined in several subtly different ways when applied to memory model specifications, we'll use this more neutral term to avoid confusion. (The ordinary dictionary definition of "antecedes" is, appropriately, "precedes in time", and so might be a better term anyway.)

Access A antecedes access B if:

As defined here, antecedence is just a relation without any guaranteed properties beyond transitivity (the final clause) -- intrathread and interthread orderings are not required to be consistent with each other. Memory order modes impose constraints on antecedence and/or execution order -- the (partial) order of accesses in a program execution without regard for antecedence, as well as restrictions (projections) of these orders to selected events, for example overwrites to the same variable.

When represented as graphs with events as nodes and relations as links, total orders form linear lists, and partial orders form DAGs -- directed acyclic graphs. Another way to represent ordered events is to give them numerical tags. These may correspond to version numbers or timestamps that may be used and compared as Vector clocks.

Guarantees about ordering properties do not necessarily imply that any given observer could know orderings in advance or validate them after the fact. They are invariants that programmers may rely on and/or provide to help establish the correctness (or at least the possibility of correctness) of programs. For example, some data race errors result when there are not enough constraints to be sure that a given Read has only one possible Write that it could read from.

Plain mode

Plain mode applies to syntactic accesses of plain (non-volatile) object fields (as in int v = aPoint.x), as well as statics and array elements. It also applies to default VarHandle get and set access. Even though it behaves in the same way as always, its properties interact with new VarHandle modes and operations in ways best explained in terms of a quick review of relevant aspects of processor and compiler design.

Plain mode extends the otherwise unnamed "Local" mode in which all accesses are to method-local arguments and variables; for example, the code for pure expressions and functions. Plain mode maintains local precedence order for accesses, which need not match source code statement order or machine instruction order, and is not in general even a total (sequential) order. To illustrate, consider this expression-based statement, where all variables are method-local ints:

  d = (a + b) * (c + b);
This might be compiled into register-based machine instructions of the form (where the r's are registers):
  1: load a, r1
  2: load b, r2
  3: add r1, r2, r3
  4: load c, r4
  5: load b, r5
  6: add r4, r5, r6
  7: mul r3, r6, r7
  8: store r7, d
But compilers are allowed to make some different choices when mapping the original tree-like (and parallelizable) expression into a sequential instruction stream. Among several legal options would be to reorder the first two instructions. This is one application of parallel Commutativity: in this context, these operations have the same effect whether executed in either order, or even at the same time (regardless of Java left-to-right expression evaluation rules).

Such decisions by compilers about instruction order might not matter much, because commodity processors themselves perform parallel ("out-of-order") instruction execution. Most processors control execution by tracking completion dependencies, using the same techniques seen when programming CompletableFutures. For example, all of the loads may be started early, triggering the add instructions (possibly in parallel on superscalar CPUs) when the values are available in registers, and similarly triggering the multiply when the adds complete. Even if two instructions are started in sequential order, they may complete (commit) in a different order, or at the same time. So local precedence, defined with respect to these commit points, need not include any before/after ordering relation among some actions. For example, one possible execution might look like (where instructions on the same line operate in parallel):

 load a, r1 | load b, r2 | load c, r4 | load b, r5
 add r1, r2, r3 | add r4, r5, r6
 mul r3, r6, r7
 store r7, d

The operations appearing on each line need not actually execute in parallel -- any sequential permutation of each line is also allowed; instructions emitted by a compiler could be correspondingly reordered. Any of these executions might occur even if the original source split the evaluation as:

  tmp = (a + b);
  d = tmp * (c + b);
In other words, the use of semicolons as statement separators need not have a direct impact on the sequencing of execution. This bothers some people (see The Silently Shifting Semicolon). But it is an effective means of recovering some of the fine-grained parallelism that exists in source code but otherwise becomes lost in machine code. And if your goal is to maximize parallelism, it turns out to be helpful that parallelism is automatically enabled at the lowest level of processing. Most users are happy enough about the consequent performance improvements to buy and use systems with increasingly aggressive out-of-order execution, due to the combined efforts of source-level compilers, tools, JIT compilers, and processors, mainly to reduce the impact of memory latency.

With the help of optimizing compilers, processors may eliminate unnecessary computations and accesses in a code body using dataflow analyses, and may remove unnecessary dependencies using transformations such as SSA and renaming. Different processors and compilers vary in how extensively they perform such optimizations. For example, some may speculatively execute code within an unevaluated conditional, undoing and throwing away effects if the condition is false. And so on. Method boundaries need not define the boundaries of such transformations. Methods may be inlined or interprocedurally optimized, even to the extent that an entire thread is one code body.

Even though computation may be parallel by default at the instruction level, in Local mode, the observable results of dependency-triggered execution are always equivalent to those of purely step-wise sequential execution, whether or not any allowed optimizations actually occur. The exact relationships between statement order and execution order that maintain the associated uniprocessor semantics don't matter, and cannot even be detected (except possibly by tools such as debuggers). There are no source-level programmer controls available to alter these relationships.

This also holds across isolated thread-confined variables -- those created and only ever used by the current thread. A thread-confined variable has no accesses in the interthread read-by relation, as may be discovered by compiler escape analysis, in which case the variable may then be treated as if it were local.

Similar properties hold in full Plain mode when all variables are accessed by only one thread throughout the course of a code region; for example, when all are correctly protected by a lock in a synchronized method. All Plain accesses within the region, possibly excepting initial reads and final writes, are within-thread, and may transiently act as if thread-confined. Definitions of stronger modes below are phrased only in terms of interthread accesses, because the per-variable constraints they add are subsumed by uniprocessor semantics that hold for strictly local accesses.

However, when Plain mode is used with variables concurrently accessed by multiple threads (i.e., in the presence of data races), mismatches between statement order and the ordering (or lack thereof) of variable accesses are often observable. Not only may accesses detectably occur in different orders, they may not occur at all. For example optimized execution of int a = p.x; ... int b = p.x may replace the second read with int b = a, and execution of p.x = 1; ... p.x = 2 may eliminate the first write, and even the second if p is not used again in a thread body. Extra writes not present in source code must not occur, but writes may appear to be "prescient" -- observably issued before they are programmed to occur: Among the craziest-seeming cases result from r1 = x; y = r2; acting as if reordered to y = r2; r1 = x; when the value of y impacts the value assigned to x in another thread. Also, when multiple other threads read Plain mode writes, some may see them in different orders than others.

Possible outcomes include those in which it might seem as if processors make mistakes assuming the presence of executions (multithreaded schedules) that do not actually occur; for example (but not limited to) executions in which no other thread concurrently executes, as well as those stemming from inter-thread compiler analyses and optimizations. Conversely, data race errors may result from programmers assuming the absence of (or not noticing the possible presence of) executions that do actually occur. Some of these problems are common enough to have been given names. For example, in transaction systems, "nonrepeatable read" errors occur when two reads of the same variable in a transaction body obtain different values, but correctness relies on the values being the same.

Additionally, while Java Plain accesses to int, char, short, float, byte, and reference types are primitively bitwise atomic, for the others, long, double, as well as compound Value Types planned for future JDK releases, it is possible for a racy read to return a value with some bits from a write by one thread, and other bits from another, with unusable results.

Due to any combination of these mechanisms, in racy programs, there may be only a weak and complicated observed relation between source code and the order of variable accesses. Unfortunately, formal memory model specifications must characterize the boundaries of this relation, which remains an unsolved problem (although there are some promising in-progress approaches). If specifications are too weak, they unintentionally allow dangerously wrong executions, including "out of thin air" reads that would violate Java safety and security guarantees. But if too strong, they unintentionally disallow valid optimizations. (And in the absence of data races, they must maintain the "uniprocessor" property that effects are equivalent to execution in source program order.)

However, the details hardly matter. None of the access constraints for shared variables needed in multithreaded programs are reliably maintained without explicit control. Failure to provide control can lead to nearly inexplicable outcomes. With a few exceptions noted below, you might treat any two Plain mode accesses in a code body as if they are unordered with respect to each other, and then subject to further optimization. Experience has shown that guesses about guaranteed orderings in Plain mode are often wrong, which is one basis for the good advice to avoid Plain mode data races. If a program requires ordering (and/or atomicity) for correctness, then arrange shared variable access explicitly, and restrict Plain mode to local computation on values.

Notes and further reading

Plain mode is usable in the same contexts as plain "non-atomic" mode in C/C++11. Java and C/C++ have many semantic differences, some stemming from the fact that in C/C++, failure to use a language construct in specified ways (including allowing data races in Plain mode) may result in Undefined Behavior, which may include program crashes. In Java, effects may be surprising (and currently incompletely specified), but are still circumscribed. These differences do not impact most usages of memory ordering modes, that are otherwise defined similarly (with some naming differences) across languages.

Unlike the case for programming languages, hardware memory models can define the analog of local precedence ("preserved program order") by exhaustively enumerating effects of instructions. For examples, see "A Tutorial Introduction to the ARM and POWER Relaxed Memory Models" by Luc Maranget, Susmit Sarkar, and Peter Sewell. GPUs extend such rules to instructions that operate on multiple (contiguous) variables at a time, and often include memory types or modes in which variables cannot be shared across threads.

Dependency-based techniques are seen at a higher level in Java parallel Streams, CompletableFutures, Flows, and other fluent APIs based on expressions describing possibly-parallel computations. Across these, automating parallelism relies on representing (or reconstructing) program fragments as DAG, not sequences. Some dataflow languages offer syntax more directly corresponding to DAG. Like fluent APIs, they can avoid confusion about questions such as whether semicolons indicate sequencing, but encounter others about scopes and contexts. (If there were a perfect way to express parallelism and ordering, we'd all be using it.)

In transactional approaches to concurrency, transaction code bodies are explicitly delimited by programmers, rather than implicit in the structure of a program. They encounter the same underlying issues, sometimes described using different terminology. For example, races are usually categorized in terms of isolation levels, not ordering constraints.

Many higher-level APIs can be designed in terms of commutative operations that enable more parallelism by requiring less ordering control, as discussed in "The Scalable Commutativity Rule: Designing Scalable Software for Multicore Processors" by Clements et al, TOCS 2015.

Approaches to avoiding mis-specification of the relation between source program order and Plain mode include "promising" semantics, in which writes are conceptually assigned timestamp version numbers, and operational rules avoid assigning impossible ones. (The operational approach to formulating such rules is similar to that of the original JLS version 1 memory model, but not the current (JLS5-9) JMM, that is expressed in terms of ordering relations.)

Opaque mode

Opaque mode, obtained using VarHandle getOpaque and setOpaque, adds constraints over Plain mode that provide minimal awareness of a variable subject to interthread access when all accesses use Opaque (or stronger) mode:

The name "opaque" stems from the idea that shared variables need not be read or written only by the current thread, so current values or their uses might not be known locally, requiring interaction with memory systems. However, Opaque mode does not directly impose any ordering constraints with respect to other variables beyond Plain mode. So you cannot always tell when Opaque mode will access a variable with respect to other plain accesses, and reading a value in Opaque mode need not tell you anything about values of any other variables. Also, while coherence ensures some ordering, it does not in itself guarantee specific orderings, in particular about read-write pairs (RMW and CAS operations described below extend coherence to do so.) Despite these limitations, Opaque mode still sometimes usefully applies. For example, when monitoring and collecting progress indicators issued by multiple threads, it may be acceptable that results only eventually be accurate upon quiescence.

Opaque mode also applies when surrounding ordering constraints described below are strong enough that further ordering control has no impact, although just using Plain mode here is also OK for primitively atomic variables. In other cases, other ordering constraints in a program, combined with per-variable ordering requirements, may provide acceptable bounds on when accesses occur and/or their possible values.

When applied to thread-confined variables (i.e., those without accesses in the interthread read-by relation), none of the above constraints can impact observable behavior, so Opaque mode access may be implemented in the same way as Local Plain access (which also acts coherent from the perspective of the executing thread.).

Notes and further reading

Opaque mode was inspired by Linux "ACCESS_ONCE". It is usable in the same contexts as C++ atomic memory_order_relaxed. While other implementations of Opaque mode are possible, on current platforms, JVMs atomically read or write the variable in program order, and rely on underlying cache coherence mechanisms to maintain the above properties, disabling the secondary caching of values in locals and registers allowed in Plain mode. (Even though Opaque mode layers constraints over Plain mode, implementation-wise, Plain mode can be thought of as layering additional local caching optimizations over Opaque mode.) As a byproduct, Opaque mode is sometimes heuristically useful for artificially disabling some optimizations in microbenchmarks. Hardware implementations (as well as detailed specifications) of coherence vary across platforms, and continue to evolve. Some processors and GPUs support special non-coherent modes designed primarily for bulk transfer that are not currently accessible from Java, but may be used when applicable by JVMs.

It is almost never a good idea to use bare spins waiting for values of variables. Use Thread.onSpinWait, Thread.yield, and/or blocking synchronization to better cope with the fact that "eventually" can be a long time, especially when there are more threads than cores on a system.

Most presentations of eventual consistency and techniques that exploit it, especially in distributed settings, apply it to stronger modes (where it also holds). See for example: "Coordination Avoidance in Database Systems" by Peter Bailis et al. VLDB 2015.

Release/Acquire (RA) mode

Release/Acquire (or RA) mode is obtained using VarHandle setRelease, getAcquire and related methods, and adds a causality constraint to Opaque mode. This can be expressed as a property of interthread accesses:

For each variable, the antecedence relation, restricted to interthread Release/Acquire (or stronger) accesses, is a partial order.
Plus intrathread rules that extend (cumulate) antecedence properties to the indicated possibly weaker mode accesses within the same Thread T (see below for some caveats about Plain mode):

This is the main idea behind causally consistent systems, including most distributed data stores. Causality is essential in most forms of communication. For example, if I make dinner, and then I tell you that dinner is ready, and you hear me, then you can be sure that dinner exists. Preserving causal consistency means that, upon hearing "ready", you have access to its cause, "dinner". Of course, RA mode has no understanding of this relationship; it just preserves orderings. The partial order property means that causality is not cyclic. As a minimal code example assuming that the only variable accesses are the ones shown:

 volatile int ready; // Initially 0, with VarHandle READY
 int dinner;         // mode does not matter here

 Thread 1                   |  Thread 2
 dinner = 17;               |  if (READY.getAcquire(this) == 1)
 READY.setRelease(this, 1); |    int d = dinner; // sees 17
This would not necessarily hold if ready were used in a weaker mode. Most usages don't employ a ready signal; instead, the producer writes a reference to the data, and the consumer reads the reference and dereferences it. As in:
 class Dinner {
   int desc;
   Dinner(int d) { desc = d; }
 volatile Dinner meal; // Initially null, with VarHandle MEAL

 Thread 1                   |  Thread 2
 Dinner f = new Dinner(17); |  Dinner m = MEAL.getAcquire();
 MEAL.setRelease(f);        |  if (m != null)
                            |    int d = m.desc; // sees 17

The causality guarantee of RA mode is needed in producer consumer designs, message-passing designs, and many others. Nearly all java.util.concurrent components include causal ("happens-before") consistency specifications in their API documentation.

RA mode is rarely strong enough to guarantee sensible outcomes when it is possible for two or more threads to write the same variable at the same time. Most recommended usages can be described in terms of ownership, in which only the owner may write, but others may read. As a basis for such reasoning, when a thread initially constructs an object, it is the sole owner until somehow making it available to other threads. Designs may additionally rely on a release-acquire pair acting as an ownership transfer -- after making an object accessible, the (previous) owner never uses it again. Some special-purpose components, such as single-producer queues, impose this restriction as a condition that component users must obey. Also, locks can be used to ensure transient ownership, and in that sense extend Release/Acquire techniques.

RA Fences

It is possible to use RA mode in a more explicit fashion, that also illustrates how it strengthens local ordering constraints. Instead of X.setRelease(this, v), you can use Opaque mode (or Plain mode if x is primitively bitwise atomic), preceded with a releaseFence: VarHandle.releaseFence(); x = v;. And similarly, instead of int v = X.getAcquire(this), you can follow a Plain or Opaque mode access with an acquireFence: int v = x; VarHandle.acquireFence().

A releaseFence ensures that all ongoing accesses (non-local writes and reads) complete before performing another write. It is a "fence" in the sense of separating all preceding accesses vs all following writes. Similarly, an acquireFence ensures that all ongoing reads complete before performing another access. If an acquireFence separates two reads, the second read cannot reuse an old value it saw before the fence -- an acquireFence "invalidates" prior reads. Note that fence method statements are among the few contexts in which using a semicolon does impact sequencing. However, effects may be interleaved with any strictly local accesses and computation, so still do not necessarily literally operate in source code order. Similarly, in an expression such as X.getAcquire() + Y.get() left-to-right evaluation order is preserved.

This encoding shows that, treated as events, RA fences themselves conform to the primary partial order (relying when necessary on inter-core hardware-level protocols), and the accesses are "carried" by the local precedence rules. However, RA mode accesses are not necessarily implemented using fences. They are instead defined to allow use of special-purpose access instructions when available, as well as to enable several possible optimizations. In particular, within-thread RA accesses may be implemented in the same way as Local Plain mode, or even eliminated entirely, as long as all other required constraints hold.

Mixed Modes and Specializations

Release and Acquire operations provide relatively cheap ways to enable communication among threads. People have discovered techniques and idioms that can make some of these effects even cheaper to obtain.

When preserving causal consistency in a software component, it is not usually necessary to use Release/Acquire mode for every access. For example, if you read a reference to a Node with immutable fields in Acquire mode, you can use Plain mode to access the Node's fields.

Other cases may require more care to obtain intended effects. It is generally good practice to read a value using getAcquire (once) into a local variable (possibly marking it final for emphasis) before using in Plain mode computations, ensuring that each use will have the same value; java.util.concurrent code uses this SSA (static single assignment) based convention to improve confidence about its correctness. Beware of compiler optimizations that may eliminate the need to access variables. For example, if an optimizing compiler determines that some variable x cannot be zero, then if (X.getAcquire(this)!=0) might not have any ordering effect. Similarly, if an optimizing compiler can precompute some or all parts of a Plain mode access expression (for example, an indexed array read), then placing the access itself after an Acquire operation might not have the expected effect. Also note that a Release mode write does not ensure that the write be issued "immediately"; it is not necessarily ordered with respect to subsequent writes.

Overall, moded accesses tend to be easier for compilers to optimize than fences when they are thread-local, and so can reduce overhead when conservatively thread-safe code is run in single-threaded contexts. On the other hand, explicit fences are easier to manually optimize, and apply in cases where ordering control is not bound to a particular variable's access. For example, in some optimistic designs, a number of possibly-Plain reads must be followed by an acquireFence before performing a validation step. And in some factory designs, a number of possibly-Plain mode writes constructing a set of objects must be followed by a releaseFence before they are published. (These correspond to the optimization of moving acquireFences as early as possible, and releaseFences as late as possible, in both cases sometimes allowing them to be merged with others.)

Because they impose fewer ordering constraints, RA accesses and fences are expected to be cheaper than Volatile mode operations, with respect to both overhead and opportunities for parallelism when mapped to processors. Compilers must issue code that maintains constraints with respect to platform-level memory models. It might be nice if all processors had instructions or rules corresponding exactly to memory order modes, but none do, so details of effects across them may differ. On TSO (including x86) systems, usage need not result in any extra machine instructions. On some other systems, acquires may be implemented using control instructions in accord with machine-level dependency rules. Some processors (including ARM) support a StoreStore fence that is cheaper than a releaseFence, but can be used in Release/Acquire mode only when it is known that load-store fencing cannot (or is not required to) make a difference; i.e., that it is either impossible or acceptable for an earlier read to return a value that was modified as a consequence of the later write. The VarHandle class includes StoreStoreFence as well as symmetric loadLoadFence methods that may loosen the associated RA ordering constraints when applicable. These methods exist to allow micro-optimizations that might improve performance on some platforms, without impacting others one way or the other.

As a delicate (but commonly exploited) special case of the above considerations, acquire-style reads of immutable fields of newly constructed objects do not require an explicit fence or moded access -- Plain mode reads suffice: If the consumer has not seen the new object before, it cannot have stale values that it must otherwise ignore or discard, and it cannot perform the read until it knows the address. On subsequent encounters, reusing old values is OK, because they cannot have changed. This reasoning rests on the only known defensible exception to the rule of never making assumptions about local precedence order: The reference (address) of a new object is assumed never to be known and impossible to speculate until creation. This assumption is also relied on by other Java security requirements.

The resulting techniques are used in Java final field implementations, and are the reason why specified guarantees for final fields are conditional upon constructors not leaking references to objects before constructor return. Classes with final fields are normally implemented by issuing a form of Release fence upon constructor return. Further, because nothing need be guaranteed about interactions with reads by the constructor, a StoreStoreFence suffices. Similar techniques may apply in other contexts, but can be unacceptably fragile. For example, code that works when the associated objects are always newly constructed may, without further safeguards, fail upon later changes to instead recycle the objects from pools.

Notes and further reading

Release mode writes are compatible with C++ atomic memory_order_release, and Acquire mode reads are compatible with memory_order_acquire. Detailed specifications underlying some of the optimizations described above await revision of the formal memory model. In java.util.concurrent.atomic classes, method setRelease replaces the equivalent but poorly named lazySet (and similarly Unsafe.putOrderedX). The Rust language enforces ownership tracking that can help ensure appropriate use of RA constructions. The reasoning behind final fields is also seen in C++ memory_order_consume, which is not available as a distinct mode in Java. The temporal definition of causality in terms of antecedence is only one facet of broader treatments of causality (see Wikipedia for example).

Volatile mode

Volatile mode is the default access mode for fields qualified as volatile, or used with VarHandle setVolatile, getVolatile and related methods. It adds to Release/Acquire mode the constraint:

(Interthread) Volatile mode accesses are totally ordered.

When all accesses use Volatile mode, program execution is sequentially consistent, in which case, for two Volatile mode accesses A and B, it must be the case that A precedes execution of B, or vice versa. In RA mode, they might be unordered and concurrent. The main consequences are seen in a famous example that goes by the names "Dekker", "SB", and "write skew". Using "M" to vary across modes:

    volatile int x, y; // initially zero, with VarHandles X and Y

    Thread 1               |  Thread 2
    X.setM(this, 1);       |  Y.setM(this, 1);
    int ry = Y.getM(this); |  int rx = X.getM(this);
If mode M is Volatile, then across all possible sequential orderings of accesses by the two threads, at least one of rx and ry must be 1. But under some of the executions allowed in RA mode, both may be 0.

Volatile Fences

It is possible to obtain the effects of Volatile mode using fences. The VarHandle.fullFence() method separates all accesses before the fence vs all accesses after the fence. Further, fullFences are globally totally ordered. So the effects of a Volatile write can be arranged using a Release mode write (or its manual version using a releaseFence), followed by fullFence(). And a Volatile read as a fullFence() followed by an Acquire mode read. This encoding ensures that Volatile accesses are totally ordered by separating them with totally ordered fences, which are in general more expensive than RA fences. Any pair of Volatile mode accesses needs only one fullFence (not two) between them, which can make usages difficult to optimize in a modular fashion when accesses may occur across different methods. The "trailing fence" convention reduces overhead by always encoding writes (for primitively bitwise atomic x) as releaseFence(); x = v; fullFence(); and reads as int v = x; fullFence(); (fullfence includes the effects of acquireFence; using just acquireFence here would emulate Acquire mode; see below). This convention matches the general tendency for reads to be much more common than writes.

Volatile mode accesses are not necessarily implemented in this way. Some processors support special read and write instructions that do not require use of fences. Others may support instructions that are less expensive than separation using fullFence. Also, access methods are defined to allow the same kinds of optimizations as RA mode. In particular, within-thread accesses could be implemented in the same way as Local Plain mode, or even eliminated entirely, as long as all other constraints hold. In some contexts, using Volatile access with a thread-confined variable might indicate a conceptual error.


Total ordering constraints provide a basis for ensuring consensus -- momentary agreement among threads about program state, such as whether a lock has been acquired, whether all of a set of threads have reached a barrier point, or whether an element exists in a collection. (If this is not immediately obvious to you, you might be comforted that it took several years and missteps before discovery of Dekker's algorithm that extends the above construction to provide a simple form of two-party lock, and more years to generalize the idea.) Implementations of update methods in most general-purpose concurrent components require at least one consensus operation (including cases where one is needed just to obtain a lock), as explained in "The Laws of Order" by Hagit Attiya et al, POPL 2011.

As discussed in Herlihy and Shavit's book The Art of Multiprocessor Programming, there's a hierarchy of operations in which each higher-consensus-number operation can be used to ensure some form of agreement that is impossible in "wait-free" bounded time/space using only lower-consensus-number operations. The three most commonly useful categories are available:

Total ordering constraints can also be used to control these operations across multiple variables. For example, Dekker-like constructions arise in the implementations of most blocking (queued) locks and related synchronizers: a releasing thread writes to lock status X, and then reads from a queue Y to see if it must signal a waiter. A waiter thread first CASes an entry into queue Y to add itself, and then (re)checks and attempts to CAS X to acquire the lock, suspending on failure. Using Volatile accesses and/or fences here avoids liveness errors in which the releaser misses seeing that a waiter needs signaling, and the waiter also misses seeing that the lock is available before suspending. The underlying idea was used in one of the earliest concurrency control constructs ever devised (in the early 1960s), Semaphores. It still applies in most concurrent components performing resource management.

Mixed Modes and Specializations

Total ordering guarantees may be overly constraining in some contexts, as illustrated by another famous example that goes by the names "IRIW" (independent reads of independent writes) and "long fork". Again using "M" to vary across modes:

    volatile int x, y; // initially zero, with VarHandles X and Y

    Thread 1                |  Thread 2                | Thread 3               | Thread 4
    X.setVolatile(this, 1); |  Y.setVolatile(this, 1); | int r1 = X.getM(this); |  int r3 = Y.getM(this);
                            |                          | int r2 = Y.getM(this); |  int r4 = X.getM(this);
If mode M is volatile, threads 3 and 4 must see the writes by threads 1 and 2 in the same order, so it is impossible for execution to result in r1==1, r2==0, r3==1, and r4==0. But if M is Acquire, this non-multicopy atomicity is allowed. There don't seem to be practical applications of IRIW-like constructions in which this ordering constraint is required or desirable. In which case, Acquire mode reads may be used instead. On TSO processors including x86, usages of Volatile-read and Acquire-read may have the same implementation, but on others, Acquire mode is expected to be cheaper.

Combinations of Volatile mode updates (writes, RMW, CAS, fences) with Acquire mode reads apply in most concurrent components. This permits partial (vs total) ordering only when reads are not paired with updates, which matches the intent of most concurrently readable data structures and other read-mostly classes with methods that advertise and maintain causal consistency, but sometimes internally employ stronger ordering due to the algorithmic need for consensus operations. This also corresponds to common goals when dealing with races: Write-Write conflicts and Read-Write conflicts must be controlled, but usually not Read-Read conflicts. This is the same idea behind Read-Write locks, but without explicit locking.

In mixed mode usages where the linearization points at which threads must agree on the outcome of a consensus operation rely on the presence of fullFence operations demarcating commitment points, explicit use of fences may simplify implementation and analysis. In particular, while Volatile writes and reads of a variable act as if they are separated by a fullFence, there is no requirement about when that fence may occur (if at all, in the case of thread-confinement). For example, the above Dekker construction is not guaranteed to work using Volatile mode write and Acquire mode read. However, it would suffice to use Release mode write, followed by an explicit fullFence, followed by Acquire mode read. Or, when applicable, Volatile mode CAS, followed by Acquire mode read.

RMW and CAS operations are also available in RA mode, supporting Acquire-only or Release-only forms. These forms maintain the strong per-variable ordering guarantee of the operation, but relax constraints for surrounding accesses. These may apply, for example when incrementing a global counter in Release mode, and reading it in Acquire mode. Not all processors support these weaker forms, in which case (including on x86) they are implemented using the stronger forms. Also, in principle, compilers may combine serially commutative RMWs such as getAndAdd as if they were parallel (when the individual return values are not used), as in combining adjacent getAndAdd(_,1)'s into getAndAdd(_,2).

On some platforms, RMW and CAS operations are implemented using an instruction pair generically known as LL/SC -- "load-linked" and "store conditional". These provide only the coherence properties of the operation: store-conditional writes and returns true if the write is guaranteed to directly follow the load-linked read in coherence order. (If returning false, the hardware cannot guarantee that no other intervening write occurred.) On these platforms (including POWER and most versions of ARM), RMW or CAS operations may loop. To enable fine-tuning, VarHandle method weakCompareAndSet (weakCAS) is loopless when implemented using LL/SC, returning false if either the store-conditional failed (usually due to contention), or the current value doesn't match expected value. In usages where retries are needed in either case, using weakCAS can be more efficient. On other platforms (such as x86), the method is equivalent to plain CAS. Also, on processors that do not directly support RMW operations, they are implemented using CAS or weakCAS.

Notes and further reading

Volatile mode is compatible with C++ atomic memory_order_seq_cst. Because total ordering of reads is not usually desired, there has been controversy among implementers about supporting it by default on non-TSO processors, where it can be costly. The introduction of RA mode allows programmers to choose. Alternatives to linearizability proofs applicable across these are the subject of active research; see for example work by the MPI-SWS Verification group.

The idea of consensus can be extended to multiple variables. Some Hardware Transactional Memory systems provide extensions of CAS and LL/SC that atomically operate on more than one variable at a time. These are not yet available from Java.

Distributed data stores lack hardware coherence-based consensus mechanisms so must rely on protocols such as Paxos. In these systems, some variant of Volatile mode (most often applied to transactions, not single accesses) is usually called "strong consistency", some variant of RA "weak consistency", and further variants that later detect and repair conflicts in weakly consistent operations are "strong eventual consistent". For a survey that tries to clarify some of this terminology, see "Consistency in Non-Transactional Distributed Storage Systems" by Paolo Viotti and Marko Vukolic, ACM Computing Surveys 2016.


Locked modes correspond to use of built-in synchronized blocks, as well as use of java.util.concurrent locks such as ReentrantLock.

In terms of ordering, acquiring locks has the same properties as Acquire mode reads, and releasing locks the same as Release mode writes. When applied to locks, the associated ordering constraints are sometimes known as "roach motel" rules -- they allow earlier reads and later writes to "move in" to lock regions, but those inside a region cannot move out.

Lock usages cannot rely on details, which may vary across lock implementations. Assuming the absence of deadlock, exclusively locked regions are serializable: They execute under mutual exclusion in some sequential order. Most pure spin-locks can use RA mode operations to control this (for example compareAndSetAcquire and setRelease). However, as noted above, most general-purpose locks require Volatile ordering upon lock release and/or acquisition to control blocking and signaling. Also, under biased locking, when a lock is initially used by only one thread, heavier operations enabling others to access the lock may be postponed until threads reach safe points that are otherwise mainly used for triggering garbage collection. And synchronized(x) blocks where x is local or thread-confined may be optimized away entirely.

Additional allowed transformations include lock coarsening: Two adjacent locked regions on the same lock can be combined, as in synchronized(p) { b1; };synchronized(p) { b2; } transformed to synchronized(p) { b1; b2; }. Doing so acts as if the accesses performing unlock of the first block and the acquire of the second are guaranteed to be within thread, which allows them to be optimized away. The resulting block can be further combined a finite number of times with other adjacent blocks. (This must be finitely bounded to maintain progress guarantees described above.)

Locking operations introduce blocking (suspension) and scheduling policies, that fall outside the scope of this document. For specifications of the main accessible blocking primitive, see documentation of park/unpark in class LockSupport. See for example the API documentation for ReentrantLock, StampedLock, and other java.util.concurrent components for more detailed specifications of scheduling policies used in JDK.

Cycling back, locks of various kinds can be used to establish mutual exclusion, that in turn enables the reliable use of Plain mode in locked code bodies. Depending on the relative costs of locks versus ordering control instructions, uncontended lock-based code can be faster than explicitly ordered lock-free code, although with the added risks of deadlock and poorer scalability under contention. (In other words, sometimes a sequential bottleneck is the best solution available short of redesign.)

The above techniques and components can also be used in the implementation of Threads themselves to arrange that thread bodies begin with Acquire and end with Release operations. Similarly, callers of Thread.start perform a Release operation and callers of Thread.join an Acquire operation.


The accounts in this guide are compatible with (and extend) the JSR133 (JLS5-8) Specification (except for corrections required even in the absence of respecification). But instead of phrasing descriptions in terms of deviations from sequential consistency, they approach programming issues from the perspective of controlling parallelism. This reflects the main usage impact of VarHandle memory order modes, that can be described in terms of constraints layered over (possibly commutative) Plain mode access rules:

Intermediate points across these are available by using mixed modes and/or explicit fences, but require more attention to interactions across modes. As indicated when discussing mode definitions, a few details are pending full formal specification, but none are expected to impact usage.

Taken together, these form building blocks for creating custom memory consistency and caching protocols. But they do not in themselves provide solutions. As attributed to Phil Karlton by Martin Fowler "There are only two hard things in Computer Science: cache invalidation and naming things." Concurrent component developers are expected to solve both, so their users don't need to.

As an overall guide for developers handling this potentially explosive mixture of C4 (Commutativity, Coherence, Causality, Consensus): most concurrent components must maintain causality guarantees to be usable. But some need stronger constraints for algorithmic reasons, and some are able to employ weaker modes by (perhaps transiently) arranging partial isolation, or by relaxing internal invariants, or when still useful, weakening promises from "now" to "eventually". Further, among the main requirements of concurrent components (including especially java.util.concurrent) is for component users to be satisfied with the resulting APIs and implementations, and so have no need for any of the techniques presented here.


Thanks for comments and suggestions by Aleksey Shipilev, Martin Buchholz, Paul Sandoz, Brian Goetz, David Holmes, Sanjoy Das, Hans Boehm, Paul McKenney, Joe Bowbeer, Stephen Dolan, Heinz Kabutz, Viktor Klang, Tim Peierls,

This work is released to the public domain, as explained at Creative Commons.