This is brief brain-dump of my understanding of the JMM. Content-wise this is a rather loose post, focusing more on intuition and less on preciseness, but hopefully it doesn’t have any fundamental mistakes. One day, when I have a lot of free time, I will try to formalize the JMM in Agda or Coq, but for now English will have to do.

Memory Models

A memory model, in the most abstract sense, is a predicate on execution traces of programs. It differentiates between valid and invalid execution traces by asserting invariants on the values observed by memory reads. I have assumed the readers have some intuitive understand of what a memory model looks like.

It is immaterial whether it is the ultimately the (javac, JIT) compiler that has to respect the memory model or the machine. If a certain property of the system keeps the compiler from performing a specific reordering, it is the compiler’s job to emit appropriate barriers to prevent that reordering at the CPU level; and if it is okay for the compiler to legally make a reordering, then it doesn’t matter what the CPU thinks – the compiler may do the transform before code emission.

The Java memory model can be roughly split into two distinct sections: an axiomatic contract between the VM and the programs running on top of the VM, and an abstract, operational notion of a well formed execution.

The Contract

With a thread X trying to execute A followed by B and thread Y trying to execute C followed by D (all these being reads and writes, for simplicity’s sake) what are the possible states of the system once both the threads are done? If the system is sequentially consistent, then the system may be in a state reached by any of the following execution traces: { A B C D }, { A C B D }, { A C D B }, { C A B D }, { C A D B } or { C D A B }. More generally, in a sequentially consistent system, the total set of observable events

  • [PropertySC 1] have a total order to them – it is always possible to give a meaningful answer to the question “did X happen before Y”?

  • [PropertySC 2] respects the program order – if A happens before B in the source, A will appear to execute before B in the final execution trace.

  • [PropertySC 3] are all atomic, and immediately visible to every thread. Specifically, a read from var will always get the value written in the write to var immediately preceding var in the total order from [PropertySC 1].

Sequential consistency is nice because it allows us to stick with the comfortable illusion that threads “execute” by interleaving their instructions into one shared instruction stream. This is far from the truth in systems with true parallelism, though; and the illusion is difficult to maintain without significant performance penalty. The Java Memory Model tries to give us conditional sequential consistency – it guarantees that a correctly synchronized Java program will behave as if it were running on a sequentially consistent runtime.

Before proceeding further, we need to digress a little into the mathematical concepts of partially ordered sets (posets) and totally ordered sets. Informally, a totally ordered is a set with a relation (denoted by below) that satisfies certain axioms. You have a ⟜ a (reflexivity), (a ⟜ b) ∧ (b ⟜ c) ⇒ a ⟜ c (transitivity) and the fact that two arbitrary elements are always ordered with respect to each other (totality). It also has “antisymmetry”, but we won’t use that today. A partially ordered set is a totally ordered set without the totality axiom. In other words, in a poset you may have “neither a b nor b a” for some as and bs.

All the actions (an action being an execution of a statement by a thread) in a program form a poset with a relation called the happens-before relation. The happens-before relation, which we’ll denote with , is an abstract relation (it has nothing to do with things actually happening before other things).

A data race is defined as two conflicting accesses (accesses to the same location, at least one being a write) that aren’t ordered by . If all possible sequentially consistent execution traces of a program are free of data races, the program is correctly synchronized. [Property 0]

Once we are able to compute the relation from an execution trace (we haven’t discussed how to do that yet), [Property 0] gives us a straightforward (but tedious) way to check that a program is correctly synchronized. We enumerate all possible execution traces that are sequentially consistent (i.e. the combined instruction stream is an interleaving of the instruction streams of individual threads), compute the relation on each of those traces, and check that for all pairs of conflicting access a and b, either a b or b a. If we are able to show that all conflicting data accesses are ordered by the relation in every possible sequentially consistent execution trace, then the program is certified to be correctly synchronized, and will actually run as if it were being run on a sequentially consistent runtime.

From the programmers’ perspective, all she needs to do is ensure that conflicting data accesses are ordered by the relation (which we’ll define in a moment) somehow, and once that constraint is satisfied, she can think about her program as if it were running on a sequentially consistently runtime.

To compute the relation on an execution trace, we compute two other relations first. Both those relations form totally ordered sets with a subset of actions in the program.

The first one is the program order (we’ll denote this by p⇝) – this is an intra-thread total order which orders actions inside a single thread sequentially, as defined by the java language semantics. So if a thread executes { A; B; C; D; } then A p⇝ B, A p⇝ C, B p⇝ C etc. This too is an abstract relation and has little to do with the actual execution order of the statements: if there are no observable consequences of reordering B and C, then the runtime may do so. p⇝ does not order actions across threads, it does not have an answer to the question “is A p⇝ B” where A and B are actions executed by two different threads. So, globally, p⇝ is a partial order, but since it orders all statements within a thread, it is an intra-thread total order. p⇝ isn’t exactly a runtime artifact (modulo control dependency on runtime inputs), we can determine it by statically analyzing a program.

The second one is the synchronization order (we’ll denote this by so⇝) – this is a global (cross thread) total order on “synchronization actions”. For our purposes at this time, these “synchronization actions” are volatile reads and writes (there are others mentioned in 1, monitorenters and monitorexits are equivalent to volatile reads and writes in this context, for instance). This means given two synchronization actions, A and B, either A so⇝ B or B so⇝ A. As an aside, this isn’t true with (which orders arbitrary actions) – is partially ordered and we may have (non synchronized) actions C and D where neither C D nor D C. so⇝ respects p⇝, meaning if X and Y are synchronization actions, then (X p⇝ Y ) (X so⇝ Y). so⇝ is a runtime artifact, even with fixed inputs, two executions of a program may have different relations as so⇝.

so⇝ is used to derive the “synchronized with” relation (denoted as sw⇝). A sw⇝ B is a shorthand for “B is a volatile read of a variable var” ∧ “A is a volatile write of the same variable var” ∧ “A so⇝ B”.

is the transitive closure of the union of sw⇝ and p⇝. That is just a fancy way of saying that

  • if A p⇝ B then A B
  • if A sw⇝ B then A B
  • if A B, B C then A C.

Now that we can compute , let us run through and “prove” an example:

volatile Task queue = null;

void ThreadA() {    
  Task t = new Task(); = "foo"; // normal store
  queue = t;      // volatile store (vS)
  // t.otherName = "bar";

void ThreadB() {    
  Task q = queue; // volatile load (vL)
  if (q != null) {
    String name =;
    // String otherName = q.otherName;
    // System.out.println(otherName);

We’ll try to prove that ThreadB will either print nothing at all, or "foo". If we can somehow show that this program is correctly synchronized, then we’d be able to use [Property 0] and say that this program will obey “as if” sequential semantics. After that the proof reduces to conditioning on “is vL before or after vS in the total sequentially consistent order?”. To show that the program is race free, we start with some sequentially consistent execution trace τ. Since τ is sequentially consistent, vL is either ordered before (first case) or after (second case) vS. By [PropertySC 3] we know that this means that the vL will be null in the first case and t in the second.

The first case is the easier one – since vL sees null, the load from doesn’t happen, and we don’t print anything. In the second case:

\(\begin{aligned} & \; \texttt{vS} \; \texttt{so⇝} \; \texttt{vL} & \text{sequentially consistent trace} \\ \Rightarrow & \; \texttt{vS} \; \texttt{sw⇝} \; \texttt{vL} & \text{definition of sw⇝} \\ \Rightarrow & \; \texttt{vS} \; \texttt{↣} \; \texttt{vL} & \text{definition of ↣ (P.0)}\\ \\ & \; \left[ \texttt{ = "foo"} \right] \; \texttt{p⇝} \; \texttt{vS} \\ \Rightarrow & \; \left[ \texttt{ = "foo"} \right] \; \texttt{↣} \; \texttt{vS} & \text{definition of ↣} \\ \Rightarrow & \; \left[ \texttt{ = "foo"} \right] \; \texttt{↣} \; \texttt{vL} & \text{(P.1)} \\ \\ \text{Similarly}\\ \\ & \; \texttt{vL} \; \texttt{p⇝} \; \left[ \texttt{String name =} \right] & \text{} \\ \Rightarrow & \; \texttt{vL} \; \texttt{↣} \; \left[ \texttt{String name =} \right] & \text{(P.2)} \\ \end{aligned}\)

By transitivity of , P.1 and P.2,

\(\begin{aligned} \left[ \texttt{ = "foo"} \right] \; \texttt{↣} \; \left[ \texttt{String name =} \right] \; \text{(P.3)} & \\ \end{aligned}\)

From P.0, P.1, P.2 and P.3, we see that all possible conflicting data accesses are ordered by . This gives us as-if sequential consistency via [Property 0], and a straightforward way to reason about the output we can expect.

As an exercise, try to (formally) analyze the consequences of uncommenting the lines touching the otherName field.

Well Formed Executions

Now that the contract between the VM and the programmers has been fixed, we need some way to discover the constraints the contract places on VM authors. For instance, one way to implement the contract is to prevent all re-orderings by severely limiting compiler optimizations and emitting hardware fences around all loads and stores. While that approach may result in correct behavior, it is clearly too conservative from the performance point of view. We’d like to allow some reorderings (and “strange” behavior), and still be able to maintain the contract – ideally, our operational scheme should give the programmer sequential consistency if and only if the program is correctly synchronized.

The JMM spec tells us what a “well ordered” execution means, and this can be proved 2 to be (strictly more than) enough to provide the conditional sequential consistency contract.

While the formal abstract operational specification is somewhat convoluted (as usual, see [1]), informally they assert two important things:

  • executions are happens-before consistent: reads don’t see writes that happen after (the inverse of ) them, and they don’t see writes that were blocked by another write – r won’t see w if w w' w' r.

The JMM spec includes an example to show that (1) isn’t enough to provide the sequential consistency guarantee:

nonvolatile global int a = 0, b = 0;

ThreadA() {
  int aL = a;
  if (aL == 1) b = 1;

ThreadB() {
  int bL = b;
  if (bL == 1) a = 1;

No sequentially consistent execution trace of this program has a data race, meaning that this program is correctly synchronized, and should have “as if” sequentially consistent semantics. However, the stores to a, b and the corresponding loads aren’t ordered by , and (1) doesn’t place any guarantee on reads not seeing writes that it doesn’t have the relation with. In other words, like in a bad time travel movie, in theory there is nothing stopping aL from “seeing” the future write a = 1, setting b = 1 which is then seen by ThreadB which, in turn, sets a to 1; with the net effective result of the write b = 1 having caused itself. This is impossible of course, since it flies against causality and basic physical laws, but we’d like whatever abstract operational semantics we choose to prevent such situations a priori (all the while preserving opportunities for interesting optimizations), and to not have to analyze such situations on a case by case basis.

Moreover, Java is a “type safe” programming language – certain kinds of errors cannot arise in Java, even in a racy program. For instance, you could never load an element from a String[] and get an Integer. You also cannot have values appear out of “thin air”:

int /* non-volatile */ a;
int /* non-volatile */ b;

ThreadA() {
  int tmpa = a;
  b = tmp;

ThreadB() {
  int tmpb = b;
  a = tmp;

The above program has a data race. By the rules specified so far, in a happens-before consistent execution you could have a = b = 42 by the end of the execution of the two threads; the value 42 somehow materialized out of “thin air” (although we all know where it came from). This is similar to the previous example – the value of a written by ThreadB was somehow propagated by the value of b written by ThreadA which was somehow propagated by the value of a written by ThreadB – a causality loop!

This brings us to the second operational axiom:

  • execution proceeds in chunks, “comitting” actions in a way that the final commit results in the execution trace we’re trying to validate. Very loosely speaking, when we are at commit i, reads that are uncommitted or are in the ith commit always see the write that preceded them in the happens before relation (this is subtly different from (1)).

In the above example, (2) prevents the causal loop that (1) allowed. Specifically, as the load from a gets committed, it has to see the write dictated , which points (by an axiom I skipped for brevity) to the initializer for a, which writes 0 to a. ThreadA thus takes the correct control flow and Java doesn’t end up breaking physics.


I find the first part of the JMM vastly easier to grok than the second part (that aims to avoid causality loops). I have a hunch that defining and avoiding causality loops in terms of fixed points will make for a much cleaner set of axioms.