Memory Model

The series of posts about so called benign data races stirred a lot of controversy and led to numerous discussions at the startup I was working at called Corensic. Two bastions formed, one claiming that no data race was benign, and the other claiming that data races were essential for performance. Then it turned out that we couldn’t even agree on the definition of a data race. In particular, the C++11 definition seemed to deviate from the established notions.

What Is a Data Race Anyway?

First of all, let’s make sure we know what we’re talking about. In current usage a data race is synonymous with a low-level data race, as opposed to a high-level race that involves either multiple memory locations, or multiple accesses per thread. Everybody agrees on the meaning of data conflict, which is multiple threads accessing the same memory location, at least one of them through a write. But a data conflict is not necessarily a data race. In order for it to become a race, one more condition must be true: the access has to be “simultaneous.”

Unfortunately, simultaneity is not a well defined term in concurrent systems. Leslie Lamport was the first to observe that a distributed system follows the rules of Special Relativity, with no independent notion of simultaneity, rather than those of Galilean Mechanics, with its absolute time. So, really, what defines a data race is up to your notion of simultaneity.

Maybe it’s easier to define what isn’t, rather than what is, simultaneous? Indeed, if we can tell which event happened before another event, we can be sure that they weren’t simultaneous. Hence the use of the famous “happened before” relationship in defining data races. In Special Relativity this kind of relationship is established by the exchange of messages, which can travel no faster than the speed of light. The act of sending a message always happens before the act of receiving the same message. In concurrent programming this kind of connection is made using synchronizing actions. Hence an alternative definition of a data race: A memory conflict without intervening synchronization.

The simplest examples of synchronizing actions are the taking and the releasing of a lock. Imagine two threads executing this code:

  x = x + 1;

In any actual execution, accesses to the shared variable x from the two threads will be separated by a synchronization. The happens-before (HB) arrow will always go from one thread releasing the lock to the other thread acquiring it. For instance in:

# Thread 1 Thread 2
1 mutex.lock();
2 x = x + 1;
3 mutex.unlock();
4 mutex.lock();
5 x = x + 1;
6 mutex.unlock();

the HB arrow goes from 3 to 4, clearly separating the conflicting accesses in 2 and 5.

Notice the careful choice of words: “actual execution.” The following execution that contains a race can never happen, provided the mutex indeed guarantees mutual exclusion:

# Thread 1 Thread 2
1 mutex.lock();
2 mutex.lock();
3 x = x + 1; x = x + 1;
4 mutex.unlock();
5 mutex.unlock();

It turns out that the selection of possible executions plays an important role in the definition of a data race. In every memory model I know of, only sequentially consistent executions are tried in testing for data races. Notice that non-sequentially-consistent executions may actually happen, but they do not enter the data-race test.

In fact, most languages try to provide the so called DRF (Data Race Free) guarantee, which states that all executions of data-race-free programs are sequentially consistent. Don’t be alarmed by the apparent circularity of the argument: you start with sequentially consistent executions to prove data-race freedom and, if you don’t find any data races, you conclude that all executions are sequentially consistent. But if you do find a data race this way, then you know that non-sequentially-consistent executions are also possible.


DRF guarantee. If there are no data races for sequentially consistent executions, there are no non-sequentially consistent executions. But if there are data races for sequentially consistent executions, the non-sequentially consistent executions are possible.

As you can see, in order to define a data race you have to precisely define what you mean by “simultaneous,” or by “synchronization,” and you have to specify to which executions your definition may be applied.

The Java Memory Model

In Java, besides traditional mutexes that are accessed through “synchronized” methods, there is another synchronization device called a volatile variable. Any access to a volatile variable is considered a synchronization action. You can draw happens-before arrows not only between consecutive unlocks and locks of the same object, but also between consecutive accesses to a volatile variable. With this extension in mind, Java offers the the traditional DRF guarantee. The semantics of data-race free programs is well defined in terms of sequential consistency thus making every Java programmer happy.

But Java didn’t stop there, it also attempted to provide at least some modicum of semantics for programs with data races. The idea is noble–as long as programmers are human, they will write buggy programs. It’s easy to proclaim that any program with data races exhibits undefined behavior, but if this undefined behavior results in serious security loopholes, people get really nervous. So what the Java memory model guarantees on top of DRF is that the undefined behavior resulting from data races cannot lead to out-of-thin-air values appearing in your program (for instance, security credentials for an intruder).

It is now widely recognized that this attempt to define the semantics of data races has failed, and the Java memory model is broken (I’m citing Hans Boehm here).

The C++ Memory Model

Why is it so important to have a good definition of a data race? Is it because of the DRF guarantee? That seems to be the motivation behind the Java memory model. The absence of data races defines a subset of programs that are sequentially consistent and therefore have well-defined semantics. But these two properties: being sequentially consistent and having well-defined semantics are not necessarily the same. After all, Java tried (albeit unsuccessfully) to define semantics for non sequentially consistent programs.

So C++ chose a slightly different approach. The C++ memory model is based on partitioning all programs into three categories:

  1. Sequentially consistent,
  2. Non-sequentially consistent, but with defined semantics, and
  3. Incorrect programs with undefined semantics

The first category is very similar to race-free Java programs. The place of Java volatile is taken by C++11 default atomic. The word “default” is crucial here, as we’ll see in a moment. Just like in Java, the DRF guarantee holds for those programs.

It’s the second category that’s causing all the controversy. It was introduced not so much for security as for performance reasons. Sequential consistency is expensive on most multiprocessors. This is why many C++ programmers currently resort to “benign” data races, even at the risk of undefined behavior. Hans Boehm’s paper, How to miscompile programs with “benign” data races, delivered a death blow to such approaches. He showed, example by example, how legitimate compiler optimizations may wreak havoc on programs with “benign” data races.

Fortunately, C++11 lets you relax sequential consistency in a controlled way, which combines high performance with the safety of well-defined (if complex) semantics. So the second category of C++ programs use atomic variables with relaxed memory ordering semantics. Here’s some typical syntax taken from my previous blog post:

std::atomic<int> owner = 0

And here’s the controversial part: According to the C++ memory model, relaxed memory operations, like the above load, don’t contribute to data races, even though they are not considered synchronization actions. Remember one of the versions of the definition of a data race: Conflicting actions without intervening synchronization? That definition doesn’t work any more.

The C++ Standard decided that only conflicts for which there is no defined semantics are called data races.

Notice that some forms of relaxed atomics may introduce synchronization. For instance, a write access with memory_order_release “happens before” another access with memory_order_acquire, if the latter follows the former in a particular execution (but not if they are reversed!).


What does it all mean for the C++11 programmer? It means that there no longer is an excuse for data races. If you need benign data races for performance, rewrite your code using weak atomics. Weak atomics give you the same kind of performance as benign data races but they have well defined semantics. Traditional “benign” races are likely to be broken by optimizing compilers or on tricky architectures. But if you use weak atomics, the compiler will apply whatever means necessary to enforce the correct semantics, and your program will always execute correctly. It will even naturally align atomic variables to avoid torn reads and writes.

What’s more, since C++11 has well defined memory semantics, compiler writers are no longer forced to be conservative with their optimizations. If the programmer doesn’t specifically mark shared variables as atomic, the compiler is free to optimize code as if it were single-threaded. So all those clever tricks with benign data races are no longer guaranteed to work, even on relatively simple architectures, like the x86. For instance, compiler is free to use your lossy counter or a binary flag for its own temporary storage, as long as it restores it back later. If other threads access those variables through racy code, they might see arbitrary values as part of the “undefined behavior.” You have been warned!

Data races lead to undefined behavior; but how bad can they really be? In my previous post I talked about benign data races and I gave several examples taken from the Windows kernel. Those examples worked because the kernel was compiled with a specific compiler for a specific processor. But in general, if you want your code to be portable, you can’t have data races, period.

You just cannot reason about something that is specifically defined to be “undefined.” So, obviously, you cannot prove the correctness of a program that has data races. However, very few people engage in proofs of correctness. In most cases the argument goes, “I can’t see how this can fail, therefore it must be correct” (maybe not in so many words). I call this “proof by lack of imagination.” If you want to become a concurrency expert, you have to constantly stretch your imagination. So let’s do a few stretches.

One of the readers of my previous post, Duarte Nunes, posted a very interesting example of a benign data race. Here’s the code:

int owner = 0;
int count = 0;
std::mutex mtx;

bool TryEnter() {
    if (owner == std::this_thread::get_id()) {
        count += 1;
        return true;

    if (mtx.try_lock()) {
        owner = std::this_thread::get_id();
        return true;
    return false;

void Exit() {
    if (count != 0) {
        count -= 1;
    owner = 0;

I highlighted in blue the parts that are executed under the lock (in a correct program, Exit will always be called after the lock has been acquired). Notice that the variable count is always accessed under the lock, so no data races there. However, the variable owner may be read outside of the lock– I highlighted that part of code in red. That’s the data race we are talking about.

Try to analyze this code and imagine what could go wrong. Notice that the compiler or the processor can’t be too malicious. The code still has to work correctly if the data race is removed, for instance if the racy read is put under the lock.

Here’s an attempt at the “proof” of correctness. First, Duarte observed that “The valid values for the owner variable are zero and the id of any thread in the process.” That sort of makes sense, doesn’t it? Now, the only way the racy read can have any effect is if the value of owner is equal to the current thread’s ID. But that’s exactly the value that could have been written only by the current thread– and under the lock.

There are two possibilities when reading owner: either we are still under that lock, in which case the read is not at all racy; or we have already released the lock. But the release of the lock happens only after the current thread zeroes owner.

Notice that this is a single-thread analysis and, within a single thread, events must be ordered (no need to discuss memory fences or acquire/release semantics at this point). A read following a write in the same thread cannot see the value that was there before the write. That would break regular single-threaded programs. Of course, other threads may have overwritten this zero with their own thread IDs, but never with the current thread ID. Or so the story goes…

Brace yourself: Here’s an example how a compiler or the processor may “optimize” the program:

void Exit() {
    if (count != 0) {
        count -= 1;
    owner = 42;
    owner = 0;

You might argue that this is insane and no compiler in its right mind would do a thing like this; but the truth is: It’s a legal program transformation. The effect of this modification is definitely not observable in the current thread. Neither is it observable by other threads in the absence of data races. Now, the unfortunate thread whose ID just happens to be 42 might observe this value and take the wrong turn. But it can only observe it through a racy read. For instance, it would never see this value if it read owner under the lock. Moreover, it would never see it if the variable owner were defined as ‘atomic’:

std::atomic<int> owner = 0

Stores and loads of atomic variables are, by default, sequentially consistent. Unfortunately sequential consistency, even on an x86, requires memory fences, which can be quite costly. It would definitely be an overkill in our example. So here’s the trick: Tell the compiler to forgo sequential consistency on a per read/write basis. For instance, here’s how you read an atomic variable without imposing any ordering constraints:


Such ‘relaxed’ operations will not introduce any memory fences– at least not on any processor I know about.

Here’s the version of the code that is exactly equivalent to the original, except that it’s correct and portable:

std::atomic<int> owner = 0;
int count = 0;
std::mutex mtx;

bool TryEnter() {
    if (owner.load(memory_order_relaxed) == std::this_thread::get_id()) {
        count += 1;
        return true;

    if (mtx.try_lock()) {, memory_order_relaxed);
        return true;
    return false;

void Exit() {
    if (count != 0) {
        count -= 1;
    }, memory_order_relaxed);

So what has changed? Can’t the compiler still do the same dirty trick, and momentarily store 42 in the owner variable? No, it can’t! Since the variable is declared ‘atomic,’ the compiler can no longer assume that the write can’t be observed by other threads.

The new version has no data races: The Standard specifically states that ‘atomic’ variables don’t contribute to data races, even in their most relaxed form.

C++ Standard, (1.10.5):
[…] “Relaxed” atomic operations are not synchronization operations even though, like synchronization operations, they cannot contribute to data races.

With those changes, I believe that our “proof” of correctness may actually be turned into a more rigorous proof using the axioms of the C++ memory model (although I’m not prepared to present one). We can have our cake (correct, portable code) and eat it too (no loss of performance). And, by the way, the same trick may be used in the case of lossy counters from my previous post.

Warning: I do not recommend this style of coding, or the use of weak atomics, to anybody who is not implementing operating system kernels or concurrency libraries.


I’d like to thank Luis Ceze, Hans Boehm, and Anthony Williams for useful remarks and for verifying my assumptions about the C++ memory model.


  1. C++ Draft Standard

Back to concurrency — this time at the lowest level. Is it possible to detect a data race by looking at assembly instructions executing on an x86 multicore processor? Find out in my other blog.

By popular demand I turned my introductory webinar into a video presentation. The purpose of this 50 min presentation is to familiarize the viewer with the basic ideas of concurrent programming. Here’s the list of topics:

  • Processes vs. Threads
  • Multithreading vs. Parallelization
  • Shared Memory vs. Message Passing
  • Data Races and Atomicity Violations
  • Relaxed Memory Models
  • Sequential Consistency and DRF Guarantee
  • Risks of Concurrency
  • Debugging Concurrent Programs

Comments and suggestions for future videos are very welcome.

I only went to one talk, not because the rest was not interesting, quite the contrary, but because I worked with Joel and Hartmut on rewriting Proto. I think we essentially got it. We have the exrpession monad implemented, my “compile” function turned out to be the equivalent of Proto transform, but with much more flexibility, expression extension produced a little Lambda EDSL with placeholders for arguments and even const terminals. It works like a charm. If you don’t know what I’m talking about, I promise to finish my blog series on monads in C++ real soon now.

The talk I went to was Chris Kohlhoff talking more about Asio, the asynchronous IO library. He was showing how the new features of C++11 make his code simpler, safer, and more flexible without too much effort. In particular he found move semantics extremely helpful in reducing (or, in some cases, eliminating) the need for memory allocation in steady state–an important property when running in an embedded system, for instance. But what I liked most was his approach to solving the inversion of control problem by implementing his own coroutines. Sure, he had to abuse C++ macros, but the code was actually much more readable and reflected the way we think about asynchronous calls.

The idea is that, with coroutines, you write your code in a linear way. You say “read socket asynchronously” and then yield. The flow of control exits the coroutine in the middle, and continues with other tasks. The trick is that the rest of the coroutine becomes your completion handler. When the async call completes, the coroutine is called again, but it starts executing right after the last yield. Your flow of control moves back and forth, but your code looks linear, instead of being fragmented into a bunch of handlers. It makes you wish coroutines were part of the language, as they are, for instance, in C#.

By the way, I caught Hans Boehm while he was waiting for the airport shuttle and asked questions about memory_order_relaxed. You know, the problem is, Can a relaxed load fetch an “out of thin air” value–a value that has never been written by anybody else? What I’m getting now is that in practice this will never happen, but it’s very hard to describe this requirement formally. In other words, Can a malicious chip manufacturer in cahoots with a compiler writer come up with a system that fulfills the letter of the C++ memory model and lets you fetch an out-of-thin-air value? I think the answer is yes, because the language of the Standard is purposely vague on this topic:

(29.3.11) [ Note: The requirements do allow r1 == r2 == 42 in the following example, with x and y initially zero:

// Thread 1:
r1 = x.load(memory_order_relaxed);
if (r1 == 42), memory_order_relaxed);
// Thread 2:
r2 = y.load(memory_order_relaxed);
if (r2 == 42), memory_order_relaxed);

However, implementations should not allow such behavior.—end note ]

Hans Boehm gave a keynote address about C++11’s support for concurrency. It was a nice overview of major features and, of course, the most interesting topic, atomics and weak atomics. The official story is that if you use locks and strong atomics, you get the DRF guarantee: If the program has no data races, it will behave in a sequentially consistent manner. How do you prove that you have no data races? You enumerate all possible interleavings, and if you can’t find one where two conflicting memory accesses happen next to each other, you’re golden. That’s more or less what Java memory model guarantees (and what Posix tried to standardize). However C++ offers the programmer a way to relax sequential consistency constraints without introducing data races. Now, if you spin it this way, it sounds like a really cool thing. Hey, look, my program is data-race free! And, get this, I don’t have to suffer sequential consistency! The natural question is, what does it buy me that the C++ Standard doesn’t treat “memory_order_relaxed” accesses as data races? I would like to hear that programs with weak atomics have well defined semantics, even if the semantics are so complex that proofs of correctness of even the simplest algorithms are non-existent. But as far as I know this is not “really” true (maybe “sort of” true?). I tried to get straight answers from Hans, but he chooses his words very carefuly, like a UN diplomat. I’ll see him again at the HotPar and I’lll press him some more.

Hans’s talk was followed by Tony Van Eerd’s presentation on lock-free programming. I liked Tony’s attitude, which was “Use Locks!” Indeed, you should look at lock-free algorithms as a last resort. He showed a few examples that were hair-raising. Even the simplest lock-free linked list is a challenge. It’s really hard to spot danger areas, like the ABA problem when the node you’re pointing at gets deallocated and reallocated when you’re not looking. Your CAS succeeds, because the addresses match, but your update ends up in the great bucket in the sky. The lock-free circular queue of integers with only one thread pushing and one thread popping turned out to be a mine field. Tony claimed that it should work with weak, relaxed memory order, atomics. But, of course, no formal proof is on the horizon. I stared at the code for minutes and it sort of made sense to me, but who knows? Hans stared at it some more and tentatively murmured that it’s probably okay. The bottom line: This is some really scary stuff.

Then I spent half a day with Hartmut and Joel: Me trying to understand Proto and they trying to understand monads. I think we’ve learned a lot from each other and the new formulation of Proto using monads is getting closer and closer. We have sort of nailed the definition of a monadic “function” in C++. I think we should call these things “hybrid” monads because they blend compile-time and runtime aspects of C++. Fascinating stuff!

I am forking out my concurrency blogging to a new site, where I am actually paid to do it (who would have thought!). I promise to keep the same quality of posts as my readers came to expect from me. My first article there is about benign data races, an interesting and controversial topic.

I will still post programming-language and functional programming articles here. The next installment of the monad cycle is in the works. I’ll be talking about Haskell, C++ metaprogramming, and monads at this year’s Boostcon (May 15-20).

In my last post I made a mistake of publishing a piece of code that used C++ weak atomics–which are part of the new C++ Memory Model. I qualified the post by saying that I had no proof of correctness, and the main purpose of it was to discourage the use of weak atomics. In a way the post was successful because it did prove that weak atomics are very tricky indeed. As was pointed out to me by Anthony and Dmitriy, my implementation was incorrect (I modified the post retroactively to make this point clear). Additionally, in his blog, Anthony published a proof of the algorithm posted by Dmitriy. And that’s when the fun started…

Before I describe what happened, let me give you a bird’s eye view of the C++ Memory Model.

The Model

The purpose of a memory model is to define the semantics of concurrent operations at an abstract level–independent of the platform on which the program is running. Essentially, one has to define in what order program statements may be executed, and what memory values may be observed. The basic memory model, the one used by Java, is based on sequential consistency. Program statements are executed in program order on each processor, and the actions of multiple threads are interleaved in some (global) order. A memory load can only see the value that was written last, according to this global order.

The C++ strong atomics (the default) follow this model.

It’s the weak atomics that are trouble. Weak atomics let the programmer specify memory ordering of loads and stores that is less than sequentially consistent. Unlike the sequentially consistent model, the model for weak atomics cannot be described in terms of a state machine (this was pointed out and criticized by Herb Sutter in his Prism proposal). Instead the model is formulated as a set of global constraints imposed on executions.

In a state-machine formulation, a step the program takes is based on previous history. The step doesn’t have to be uniquely defined–the machine may be non-deterministic. The execution may split into multiple branches. What is important, though, is that the step taken does not depend on the future of the execution.

This is not true in the weak-atomics model. The way to think about it is that, in a non-sequentially-consistent execution, the values seen by memory loads may come either from the past or from the future. A processor may speculate–make a guess at the values that have not been stored yet. A speculation may be discarded if the guess turns out to be wrong, or accepted if it was right. So the acceptability of a given execution can only be determined by looking at the whole execution, from start to end. Understandably, this makes any correctness proofs extremely tricky.

The Proof

The proposed implementation of the Peterson lock using weak atomics can be reduced to the following:

// Thread 0, memory_order_relaxed);
r0 =, memory_order_acq_rel);
r1 = oneWants.load(memory_order_acquire);

// Thread 1, memory_order_relaxed);
r2 =, memory_order_acq_rel);
r3 = zeroWants.load(memory_order_acquire);

(The memory_order_acquire directives in the two loads are needed for synchronization with the unlock part of the algorithm, which is not included here.)

The hardest part of the proof is to show mutual exclusion–it should be impossible for both threads to enter the critical section at the same time. In Peterson’s algorithm, the entrance to the critical section is predicated on the values loaded into r1 and r3. If both values are false, the algorithm is broken.

Let’s consider the situation when both threads try to acquire the lock for the first time. The values of zeroWants and oneWants are initially false, and the value of victim is zero. Is it possible for both loads (zeroWants and oneWants) to see the initial values, rather than the ones saved by the opposing thread? The short answer is yes, as long as there is no intervening inter-thread synchronization.

In this case, the only synchronization that may happen is between a store-release in one thread and the corresponding load-acquire in the other. For instance, if the load part of the exchange operation on victim in thread 0 sees the value written by the store part of the exchange in thread 1 (that is, r0 is equal to 1), than these two operations are in a “synchronizes-with” relationship. We can then establish the following chronology:

  1. The save of true to oneWants in T1 happens before the store of 1 to victim (part of exchange) in T1. This is guaranteed by program order.
  2. The store of 1 into victim in T1 happens before the load of victim in T0 (part of exchange). This is because r0 is 1 in this part of the proof.
  3. The load of victim in T0 happens before the load of oneWants in T0. This is guaranteed by program order.

Because the happens-before relation is transitive, we can conclude that the load of oneWants in T0 (item 3) must see the value stored in T1 (item 1), which is true, and therefore T0 won’t enter the critical section. So far so good.

That was the simple case. Here’s the controversial one: Both r0 and r2 are zero. In particular, it means that r0 came from the initial value of victim. What about r2? It could have come either from the exchange in T0 or from the initial value of victim (both are zero!). According to the memory model, we have to consider both cases!

If r0 came from the exchange in T1 then we can use the same reasoning as before to conclude that T1 cannot enter the critical section.

But if the source of the zero in r0 is the original value of victim then there is no synchronization between T0 and T1. Both threads can enter the critical section!

You might be thinking: Either the exchange in T1 comes first and the exchange in T2 must see its value, or the exchange in T2 comes first and the one in T1 must see its value–they can’t both see the original value. This just shows you how ingrained sequentially consistent thinking is. There is no meaning to “comes first” in the world of weak atomics. Two events may only be sequenced if they occur in the same thread, or there is a synchronization path between them. Here we are still at the stage of trying to establish whether the two exchanges are synchronized or not.

The Controversy

Let me spell it out: Both exchanges see the initial value of victim. Since they didn’t interact, there is no synchronizes-with relationship between them. Therefore we can’t establish any inter-thread chronology, and it’s perfectly possible for both threads to see the original values of zeroWants and oneWants.

// Thread 0, memory_order_relaxed);
r0 =, memory_order_acq_rel); // sees initial value, 0
r1 = oneWants.load(memory_order_acquire); // sees initial value, false

// Thread 1, memory_order_relaxed);
r2 =, memory_order_acq_rel); // sees initial value, 0
r3 = zeroWants.load(memory_order_acquire); // sees initial value, false

I had concluded that the implementation was broken, but I let Anthony defend his proof.

Here’s the argument he presented, based on the modification order of victim. The C++ memory model enforces global modification order for every memory location. The victim variable starts at zero and then is modified twice by the exchanges in T0 and T1. If thread T1 sees the original value, zero, of victim then the same exchange modifies it to one. The modification order is therefore (T1: 1, T0: 0). If we assume that an atomic exchange must always see the preceding value in the modification order, then we must conclude that the exchange in T0 must read 1. Therefore it synchronizes with the exchange in T1 and mutual exclusion is assured.

And here’s the clincher: The draft C++ Standard did not specify what value must be seen by the atomic exchange (or, more generally, by an atomic Read-Modify-Write operation). I confirmed that with Hans Boehm, who concluded that this apparent hole in the standard must be corrected.

The next draft of the Standard will say that a RMW operation must see the last value in the modification order of the memory object in question.

Only then the proof of correctness of the weak-atomics implementation of Peterson lock will be complete.


I had no idea what I was getting myself into when attempting to reason about C++ weak atomics. The theory behind them is so complex that it’s borderline unusable. It took three people (Anthony, Hans, and me) and a modification to the Standard to complete the proof of a relatively simple algorithm. Imagine doing the same for a lock-free queue based on weak atomics!

Hans, who was instrumental in creating the C++ memory model, hopes that weak atomics will be a stopgap measure until processor designers find more efficient ways of implementing sequential consistency. For now, it is strongly recommended to leave weak atomics to top experts whose numbers can be counted on the fingers of one hand.

I’m grateful to (in order of appearance) Anthony Williams, Dmitriy V’jukov, Hans Boehm, and others who contributed to this post.