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32
Common Concurrency Problems
Researchers have spent a great deal of time and effort looking into con-
currency bugs over many years. Much of the early work focused on
deadlock, a topic which we’ve touched on in the past chapters but will
now dive into deeply [C+71]. More recent work focuses on studying
other types of common concurrency bugs (i.e., non-deadlock bugs). In
this chapter, we take a brief look at some example concurrency problems
found in real code bases, to better understand what problems to look out
for. And thus our central issue for this chapter:
CRUX: HOW TO HANDLE COMMON CONCURRENCY BUGS
Concurrency bugs tend to come in a variety of common patterns.
Knowing which ones to look out for is the first step to writing more ro-
bust, correct concurrent code.
32.1 What Types Of Bugs Exist?
The first, and most obvious, question is this: what types of concur-
rency bugs manifest in complex, concurrent programs? This question is
difficult to answer in general, but fortunately, some others have done the
work for us. Specifically, we rely upon a study by Lu et al. [L+08], which
analyzes a number of popular concurrent applications in great detail to
understand what types of bugs arise in practice.
The study focuses on four major and important open-source applica-
tions: MySQL (a popular database management system), Apache (a well-
known web server), Mozilla (the famous web browser), and OpenOffice
(a free version of the MS Office suite, which some people actually use).
In the study, the authors examine concurrency bugs that have been found
and fixed in each of these code bases, turning the developers’ work into a
quantitative bug analysis; understanding these results can help you un-
derstand what types of problems actually occur in mature code bases.
Figure 32.1 shows a summary of the bugs Lu and colleagues studied.
From the figure, you can see that there were 105 total bugs, most of which
1
2 COMMON CONCURRENCY PROBLEMS
Application What it does Non-Deadlock Deadlock
MySQL Database Server 14 9
Apache Web Server 13 4
Mozilla Web Browser 41 16
OpenOffice Office Suite 6 2
Total 74 31
Figure 32.1: Bugs In Modern Applications
were not deadlock (74); the remaining 31 were deadlock bugs. Further,
you can see the number of bugs studied from each application; while
OpenOffice only had 8 total concurrency bugs, Mozilla had nearly 60.
We now dive into these different classes of bugs (non-deadlock, dead-
lock) a bit more deeply. For the first class of non-deadlock bugs, we use
examples from the study to drive our discussion. For the second class of
deadlock bugs, we discuss the long line of work that has been done in
either preventing, avoiding, or handling deadlock.
32.2 Non-Deadlock Bugs
Non-deadlock bugs make up a majority of concurrency bugs, accord-
ing to Lu’s study. But what types of bugs are these? How do they arise?
How can we fix them? We now discuss the two major types of non-
deadlock bugs found by Lu et al.: atomicity violation bugs and order
violation bugs.
Atomicity-Violation Bugs
The first type of problem encountered is referred to as an atomicity vi-
olation. Here is a simple example, found in MySQL. Before reading the
explanation, try figuring out what the bug is. Do it!
1 Thread 1::
2 if (thd->proc_info) {
3 fputs(thd->proc_info, …);
4 }
5
6 Thread 2::
7 thd->proc_info = NULL;
Figure 32.2: Atomicity Violation (atomicity.c)
In the example, two different threads access the field proc info in
the structure thd. The first thread checks if the value is non-NULL and
then prints its value; the second thread sets it to NULL. Clearly, if the
first thread performs the check but then is interrupted before the call to
fputs, the second thread could run in-between, thus setting the pointer
to NULL; when the first thread resumes, it will crash, as a NULL pointer
will be dereferenced by fputs.
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The more formal definition of an atomicity violation, according to Lu
et al, is this: “The desired serializability among multiple memory accesses
is violated (i.e. a code region is intended to be atomic, but the atomicity
is not enforced during execution).” In our example above, the code has
an atomicity assumption (in Lu’s words) about the check for non-NULL of
proc info and the usage of proc info in the fputs() call; when the
assumption is incorrect, the code will not work as desired.
Finding a fix for this type of problem is often (but not always) straight-
forward. Can you think of how to fix the code above?
In this solution (Figure 32.3), we simply add locks around the shared-
variable references, ensuring that when either thread accesses the proc info
field, it has a lock held (proc info lock). Of course, any other code that
accesses the structure should also acquire this lock before doing so.
1 pthread_mutex_t proc_info_lock = PTHREAD_MUTEX_INITIALIZER;
2
3 Thread 1::
4 pthread_mutex_lock(&proc_info_lock);
5 if (thd->proc_info) {
6 fputs(thd->proc_info, …);
7 }
8 pthread_mutex_unlock(&proc_info_lock);
9
10 Thread 2::
11 pthread_mutex_lock(&proc_info_lock);
12 thd->proc_info = NULL;
13 pthread_mutex_unlock(&proc_info_lock);
Figure 32.3: Atomicity Violation Fixed (atomicity fixed.c)
Order-Violation Bugs
Another common type of non-deadlock bug found by Lu et al. is known
as an order violation. Here is another simple example; once again, see if
you can figure out why the code below has a bug in it.
1 Thread 1::
2 void init() {
3 mThread = PR_CreateThread(mMain, …);
4 }
5
6 Thread 2::
7 void mMain(…) {
8 mState = mThread->State;
9 }
Figure 32.4: Ordering Bug (ordering.c)
As you probably figured out, the code in Thread 2 seems to assume
that the variable mThread has already been initialized (and is not NULL);
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1 pthread_mutex_t mtLock = PTHREAD_MUTEX_INITIALIZER;
2 pthread_cond_t mtCond = PTHREAD_COND_INITIALIZER;
3 int mtInit = 0;
4
5 Thread 1::
6 void init() {
7 …
8 mThread = PR_CreateThread(mMain, …);
9
10 // signal that the thread has been created…
11 pthread_mutex_lock(&mtLock);
12 mtInit = 1;
13 pthread_cond_signal(&mtCond);
14 pthread_mutex_unlock(&mtLock);
15 …
16 }
17
18 Thread 2::
19 void mMain(…) {
20 …
21 // wait for the thread to be initialized…
22 pthread_mutex_lock(&mtLock);
23 while (mtInit == 0)
24 pthread_cond_wait(&mtCond, &mtLock);
25 pthread_mutex_unlock(&mtLock);
26
27 mState = mThread->State;
28 …
29 }
Figure 32.5: Fixing The Ordering Violation (ordering fixed.c)
however, if Thread 2 runs immediately once created, the value of mThread
will not be set when it is accessed within mMain() in Thread 2, and will
likely crash with a NULL-pointer dereference. Note that we assume the
value of mThread is initially NULL; if not, even stranger things could
happen as arbitrary memory locations are accessed through the derefer-
ence in Thread 2.
The more formal definition of an order violation is the following: “The
desired order between two (groups of) memory accesses is flipped (i.e., A
should always be executed before B, but the order is not enforced during
execution)” [L+08].
The fix to this type of bug is generally to enforce ordering. As dis-
cussed previously, using condition variables is an easy and robust way
to add this style of synchronization into modern code bases. In the exam-
ple above, we could thus rewrite the code as seen in Figure 32.5.
In this fixed-up code sequence, we have added a condition variable
(mtCond) and corresponding lock (mtLock), as well as a state variable
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(mtInit). When the initialization code runs, it sets the state of mtInit
to 1 and signals that it has done so. If Thread 2 had run before this point,
it will be waiting for this signal and corresponding state change; if it runs
later, it will check the state and see that the initialization has already oc-
curred (i.e., mtInit is set to 1), and thus continue as is proper. Note that
we could likely use mThread as the state variable itself, but do not do so
for the sake of simplicity here. When ordering matters between threads,
condition variables (or semaphores) can come to the rescue.
Non-Deadlock Bugs: Summary
A large fraction (97%) of non-deadlock bugs studied by Lu et al. are either
atomicity or order violations. Thus, by carefully thinking about these
types of bug patterns, programmers can likely do a better job of avoiding
them. Moreover, as more automated code-checking tools develop, they
should likely focus on these two types of bugs as they constitute such a
large fraction of non-deadlock bugs found in deployment.
Unfortunately, not all bugs are as easily fixed as the examples we
looked at above. Some require a deeper understanding of what the pro-
gram is doing, or a larger amount of code or data structure reorganization
to fix. Read Lu et al.’s excellent (and readable) paper for more details.
32.3 Deadlock Bugs
Beyond the concurrency bugs mentioned above, a classic problem that
arises in many concurrent systems with complex locking protocols is known
as deadlock. Deadlock occurs, for example, when a thread (say Thread
1) is holding a lock (L1) and waiting for another one (L2); unfortunately,
the thread (Thread 2) that holds lock L2 is waiting for L1 to be released.
Here is a code snippet that demonstrates such a potential deadlock:
Thread 1: Thread 2:
pthread_mutex_lock(L1); pthread_mutex_lock(L2);
pthread_mutex_lock(L2); pthread_mutex_lock(L1);
Figure 32.6: Simple Deadlock (deadlock.c)
Note that if this code runs, deadlock does not necessarily occur; rather,
it may occur, if, for example, Thread 1 grabs lock L1 and then a context
switch occurs to Thread 2. At that point, Thread 2 grabs L2, and tries to
acquire L1. Thus we have a deadlock, as each thread is waiting for the
other and neither can run. See Figure 32.7 for a graphical depiction; the
presence of a cycle in the graph is indicative of the deadlock.
The figure should make the problem clear. How should programmers
write code so as to handle deadlock in some way?
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Thread 1
Thread 2
Lock L1
Lock L2
Holds
Holds
W
a
n
te
d
b
y
W
a
n
te
d
b
y
Figure 32.7: The Deadlock Dependency Graph
CRUX: HOW TO DEAL WITH DEADLOCK
How should we build systems to prevent, avoid, or at least detect and
recover from deadlock? Is this a real problem in systems today?
Why Do Deadlocks Occur?
As you may be thinking, simple deadlocks such as the one above seem
readily avoidable. For example, if Thread 1 and 2 both made sure to grab
locks in the same order, the deadlock would never arise. So why do dead-
locks happen?
One reason is that in large code bases, complex dependencies arise
between components. Take the operating system, for example. The vir-
tual memory system might need to access the file system in order to page
in a block from disk; the file system might subsequently require a page
of memory to read the block into and thus contact the virtual memory
system. Thus, the design of locking strategies in large systems must be
carefully done to avoid deadlock in the case of circular dependencies that
may occur naturally in the code.
Another reason is due to the nature of encapsulation. As software de-
velopers, we are taught to hide details of implementations and thus make
software easier to build in a modular way. Unfortunately, such modular-
ity does not mesh well with locking. As Jula et al. point out [J+08], some
seemingly innocuous interfaces almost invite you to deadlock. For exam-
ple, take the Java Vector class and the method AddAll(). This routine
would be called as follows:
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Vector v1, v2;
v1.AddAll(v2);
Internally, because the method needs to be multi-thread safe, locks for
both the vector being added to (v1) and the parameter (v2) need to be
acquired. The routine acquires said locks in some arbitrary order (say v1
then v2) in order to add the contents of v2 to v1. If some other thread
calls v2.AddAll(v1) at nearly the same time, we have the potential for
deadlock, all in a way that is quite hidden from the calling application.
Conditions for Deadlock
Four conditions need to hold for a deadlock to occur [C+71]:
• Mutual exclusion: Threads claim exclusive control of resources that
they require (e.g., a thread grabs a lock).
• Hold-and-wait: Threads hold resources allocated to them (e.g., locks
that they have already acquired) while waiting for additional re-
sources (e.g., locks that they wish to acquire).
• No preemption: Resources (e.g., locks) cannot be forcibly removed
from threads that are holding them.
• Circular wait: There exists a circular chain of threads such that each
thread holds one or more resources (e.g., locks) that are being re-
quested by the next thread in the chain.
If any of these four conditions are not met, deadlock cannot occur.
Thus, we first explore techniques to prevent deadlock; each of these strate-
gies seeks to prevent one of the above conditions from arising and thus is
one approach to handling the deadlock problem.
Prevention
Circular Wait
Probably the most practical prevention technique (and certainly one that
is frequently employed) is to write your locking code such that you never
induce a circular wait. The most straightforward way to do that is to pro-
vide a total ordering on lock acquisition. For example, if there are only
two locks in the system (L1 and L2), you can prevent deadlock by always
acquiring L1 before L2. Such strict ordering ensures that no cyclical wait
arises; hence, no deadlock.
Of course, in more complex systems, more than two locks will exist,
and thus total lock ordering may be difficult to achieve (and perhaps
is unnecessary anyhow). Thus, a partial ordering can be a useful way
to structure lock acquisition so as to avoid deadlock. An excellent real
example of partial lock ordering can be seen in the memory mapping
code in Linux [T+94] (v5.2); the comment at the top of the source code
reveals ten different groups of lock acquisition orders, including simple
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TIP: ENFORCE LOCK ORDERING BY LOCK ADDRESS
In some cases, a function must grab two (or more) locks; thus, we know
we must be careful or deadlock could arise. Imagine a function that is
called as follows: do something(mutex t *m1, mutex t *m2). If
the code always grabs m1 before m2 (or always m2 before m1), it could
deadlock, because one thread could call do something(L1, L2) while
another thread could call do something(L2, L1).
To avoid this particular issue, the clever programmer can use the address
of each lock as a way of ordering lock acquisition. By acquiring locks in
either high-to-low or low-to-high address order, do something() can
guarantee that it always acquires locks in the same order, regardless of
which order they are passed in. The code would look something like this:
if (m1 > m2) { // grab in high-to-low address order
pthread_mutex_lock(m1);
pthread_mutex_lock(m2);
} else {
pthread_mutex_lock(m2);
pthread_mutex_lock(m1);
}
// Code assumes that m1 != m2 (not the same lock)
By using this simple technique, a programmer can ensure a simple and
efficient deadlock-free implementation of multi-lock acquisition.
ones such as “i mutex before i mmap rwsem” and more complex orders
such as “i mmap rwsem before private lock before swap lock before
i pages lock”.
As you can imagine, both total and partial ordering require careful
design of locking strategies and must be constructed with great care. Fur-
ther, ordering is just a convention, and a sloppy programmer can easily
ignore the locking protocol and potentially cause deadlock. Finally, lock
ordering requires a deep understanding of the code base, and how vari-
ous routines are called; just one mistake could result in the “D” word1.
Hold-and-wait
The hold-and-wait requirement for deadlock can be avoided by acquiring
all locks at once, atomically. In practice, this could be achieved as follows:
1 pthread_mutex_lock(prevention); // begin acquisition
2 pthread_mutex_lock(L1);
3 pthread_mutex_lock(L2);
4 …
5 pthread_mutex_unlock(prevention); // end
1Hint: “D” stands for “Deadlock”.
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By first grabbing the lock prevention, this code guarantees that no
untimely thread switch can occur in the midst of lock acquisition and thus
deadlock can once again be avoided. Of course, it requires that any time
any thread grabs a lock, it first acquires the global prevention lock. For
example, if another thread was trying to grab locks L1 and L2 in a dif-
ferent order, it would be OK, because it would be holding the prevention
lock while doing so.
Note that the solution is problematic for a number of reasons. As
before, encapsulation works against us: when calling a routine, this ap-
proach requires us to know exactly which locks must be held and to ac-
quire them ahead of time. This technique also is likely to decrease con-
currency as all locks must be acquired early on (at once) instead of when
they are truly needed.
No Preemption
Because we generally view locks as held until unlock is called, multiple
lock acquisition often gets us into trouble because when waiting for one
lock we are holding another. Many thread libraries provide a more flex-
ible set of interfaces to help avoid this situation. Specifically, the routine
pthread mutex trylock() either grabs the lock (if it is available) and
returns success or returns an error code indicating the lock is held; in the
latter case, you can try again later if you want to grab that lock.
Such an interface could be used as follows to build a deadlock-free,
ordering-robust lock acquisition protocol:
1 top:
2 pthread_mutex_lock(L1);
3 if (pthread_mutex_trylock(L2) != 0) {
4 pthread_mutex_unlock(L1);
5 goto top;
6 }
Note that another thread could follow the same protocol but grab the
locks in the other order (L2 then L1) and the program would still be dead-
lock free. One new problem does arise, however: livelock. It is possible
(though perhaps unlikely) that two threads could both be repeatedly at-
tempting this sequence and repeatedly failing to acquire both locks. In
this case, both systems are running through this code sequence over and
over again (and thus it is not a deadlock), but progress is not being made,
hence the name livelock. There are solutions to the livelock problem, too:
for example, one could add a random delay before looping back and try-
ing the entire thing over again, thus decreasing the odds of repeated in-
terference among competing threads.
One point about this solution: it skirts around the hard parts of using
a trylock approach. The first problem that would likely exist again arises
due to encapsulation: if one of these locks is buried in some routine that
is getting called, the jump back to the beginning becomes more complex
to implement. If the code had acquired some resources (other than L1)
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along the way, it must make sure to carefully release them as well; for
example, if after acquiring L1, the code had allocated some memory, it
would have to release that memory upon failure to acquire L2, before
jumping back to the top to try the entire sequence again. However, in
limited circumstances (e.g., the Java vector method mentioned earlier),
this type of approach could work well.
You might also notice that this approach doesn’t really add preemption
(the forcible action of taking a lock away from a thread that owns it),
but rather uses the trylock approach to allow a developer to back out of
lock ownership (i.e., preempt their own ownership) in a graceful way.
However, it is a practical approach, and thus we include it here, despite
its imperfection in this regard.
Mutual Exclusion
The final prevention technique would be to avoid the need for mutual
exclusion at all. In general, we know this is difficult, because the code we
wish to run does indeed have critical sections. So what can we do?
Herlihy had the idea that one could design various data structures
without locks at all [H91, H93]. The idea behind these lock-free (and
related wait-free) approaches here is simple: using powerful hardware
instructions, you can build data structures in a manner that does not re-
quire explicit locking.
As a simple example, let us assume we have a compare-and-swap in-
struction, which as you may recall is an atomic instruction provided by
the hardware that does the following:
1 int CompareAndSwap(int *address, int expected, int new) {
2 if (*address == expected) {
3 *address = new;
4 return 1; // success
5 }
6 return 0; // failure
7 }
Imagine we now wanted to atomically increment a value by a certain
amount, using compare-and-swap. We could do so with the following
simple function:
1 void AtomicIncrement(int *value, int amount) {
2 do {
3 int old = *value;
4 } while (CompareAndSwap(value, old, old + amount) == 0);
5 }
Instead of acquiring a lock, doing the update, and then releasing it, we
have instead built an approach that repeatedly tries to update the value to
the new amount and uses the compare-and-swap to do so. In this manner,
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no lock is acquired, and no deadlock can arise (though livelock is still
a possibility, and thus a robust solution will be more complex than the
simple code snippet above).
Let us consider a slightly more complex example: list insertion. Here
is code that inserts at the head of a list:
1 void insert(int value) {
2 node_t *n = malloc(sizeof(node_t));
3 assert(n != NULL);
4 n->value = value;
5 n->next = head;
6 head = n;
7 }
This code performs a simple insertion, but if called by multiple threads
at the “same time”, has a race condition. Can you figure out why? (draw
a picture of what could happen to a list if two concurrent insertions take
place, assuming, as always, a malicious scheduling interleaving). Of
course, we could solve this by surrounding this code with a lock acquire
and release:
1 void insert(int value) {
2 node_t *n = malloc(sizeof(node_t));
3 assert(n != NULL);
4 n->value = value;
5 pthread_mutex_lock(listlock); // begin critical section
6 n->next = head;
7 head = n;
8 pthread_mutex_unlock(listlock); // end critical section
9 }
In this solution, we are using locks in the traditional manner2. Instead,
let us try to perform this insertion in a lock-free manner simply using the
compare-and-swap instruction. Here is one possible approach:
1 void insert(int value) {
2 node_t *n = malloc(sizeof(node_t));
3 assert(n != NULL);
4 n->value = value;
5 do {
6 n->next = head;
7 } while (CompareAndSwap(&head, n->next, n) == 0);
8 }
2The astute reader might be asking why we grabbed the lock so late, instead of right
when entering insert(); can you, astute reader, figure out why that is likely correct? What
assumptions does the code make, for example, about the call to malloc()?
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The code here updates the next pointer to point to the current head,
and then tries to swap the newly-created node into position as the new
head of the list. However, this will fail if some other thread successfully
swapped in a new head in the meanwhile, causing this thread to retry
again with the new head.
Of course, building a useful list requires more than just a list insert,
and not surprisingly building a list that you can insert into, delete from,
and perform lookups on in a lock-free manner is non-trivial. Read the
rich literature on lock-free and wait-free synchronization to learn more
[H01, H91, H93].
Deadlock Avoidance via Scheduling
Instead of deadlock prevention, in some scenarios deadlock avoidance
is preferable. Avoidance requires some global knowledge of which locks
various threads might grab during their execution, and subsequently sched-
ules said threads in a way as to guarantee no deadlock can occur.
For example, assume we have two processors and four threads which
must be scheduled upon them. Assume further we know that Thread
1 (T1) grabs locks L1 and L2 (in some order, at some point during its
execution), T2 grabs L1 and L2 as well, T3 grabs just L2, and T4 grabs no
locks at all. We can show these lock acquisition demands of the threads
in tabular form:
T1 T2 T3 T4
L1 yes yes no no
L2 yes yes yes no
A smart scheduler could thus compute that as long as T1 and T2 are
not run at the same time, no deadlock could ever arise. Here is one such
schedule:
CPU 1
CPU 2 T1 T2
T3 T4
Note that it is OK for (T3 and T1) or (T3 and T2) to overlap. Even
though T3 grabs lock L2, it can never cause a deadlock by running con-
currently with other threads because it only grabs one lock.
Let’s look at one more example. In this one, there is more contention
for the same resources (again, locks L1 and L2), as indicated by the fol-
lowing contention table:
T1 T2 T3 T4
L1 yes yes yes no
L2 yes yes yes no
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TIP: DON’T ALWAYS DO IT PERFECTLY (TOM WEST’S LAW)
Tom West, famous as the subject of the classic computer-industry book
Soul of a New Machine [K81], says famously: “Not everything worth doing
is worth doing well”, which is a terrific engineering maxim. If a bad
thing happens rarely, certainly one should not spend a great deal of effort
to prevent it, particularly if the cost of the bad thing occurring is small.
If, on the other hand, you are building a space shuttle, and the cost of
something going wrong is the space shuttle blowing up, well, perhaps
you should ignore this piece of advice.
Some readers object: “This sounds like you are suggesting mediocrity
as a solution!” Perhaps they are right, that we should be careful with
advice such as this. However, our experience tells us that in the world of
engineering, with pressing deadlines and other real-world concerns, one
will always have to decide which aspects of a system to build well and
which to put aside for another day. The hard part is knowing which to
do when, a bit of insight only gained through experience and dedication
to the task at hand.
In particular, threads T1, T2, and T3 all need to grab both locks L1 and
L2 at some point during their execution. Here is a possible schedule that
guarantees that no deadlock could ever occur:
CPU 1
CPU 2 T1 T2 T3
T4
As you can see, static scheduling leads to a conservative approach
where T1, T2, and T3 are all run on the same processor, and thus the
total time to complete the jobs is lengthened considerably. Though it may
have been possible to run these tasks concurrently, the fear of deadlock
prevents us from doing so, and the cost is performance.
One famous example of an approach like this is Dijkstra’s Banker’s Al-
gorithm [D64], and many similar approaches have been described in the
literature. Unfortunately, they are only useful in very limited environ-
ments, for example, in an embedded system where one has full knowl-
edge of the entire set of tasks that must be run and the locks that they
need. Further, such approaches can limit concurrency, as we saw in the
second example above. Thus, avoidance of deadlock via scheduling is
not a widely-used general-purpose solution.
Detect and Recover
One final general strategy is to allow deadlocks to occasionally occur, and
then take some action once such a deadlock has been detected. For exam-
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ple, if an OS froze once a year, you would just reboot it and get happily (or
grumpily) on with your work. If deadlocks are rare, such a non-solution
is indeed quite pragmatic.
Many database systems employ deadlock detection and recovery tech-
niques. A deadlock detector runs periodically, building a resource graph
and checking it for cycles. In the event of a cycle (deadlock), the system
needs to be restarted. If more intricate repair of data structures is first
required, a human being may be involved to ease the process.
More detail on database concurrency, deadlock, and related issues can
be found elsewhere [B+87, K87]. Read these works, or better yet, take a
course on databases to learn more about this rich and interesting topic.
32.4 Summary
In this chapter, we have studied the types of bugs that occur in con-
current programs. The first type, non-deadlock bugs, are surprisingly
common, but often are easier to fix. They include atomicity violations,
in which a sequence of instructions that should have been executed to-
gether was not, and order violations, in which the needed order between
two threads was not enforced.
We have also briefly discussed deadlock: why it occurs, and what can
be done about it. The problem is as old as concurrency itself, and many
hundreds of papers have been written about the topic. The best solu-
tion in practice is to be careful, develop a lock acquisition order, and
thus prevent deadlock from occurring in the first place. Wait-free ap-
proaches also have promise, as some wait-free data structures are now
finding their way into commonly-used libraries and critical systems, in-
cluding Linux. However, their lack of generality and the complexity to
develop a new wait-free data structure will likely limit the overall util-
ity of this approach. Perhaps the best solution is to develop new concur-
rent programming models: in systems such as MapReduce (from Google)
[GD02], programmers can describe certain types of parallel computations
without any locks whatsoever. Locks are problematic by their very na-
ture; perhaps we should seek to avoid using them unless we truly must.
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COMMON CONCURRENCY PROBLEMS 15
References
[B+87] “Concurrency Control and Recovery in Database Systems” by Philip A. Bernstein, Vas-
sos Hadzilacos, Nathan Goodman. Addison-Wesley, 1987. The classic text on concurrency in
database management systems. As you can tell, understanding concurrency, deadlock, and other topics
in the world of databases is a world unto itself. Study it and find out for yourself.
[C+71] “System Deadlocks” by E.G. Coffman, M.J. Elphick, A. Shoshani. ACM Computing
Surveys, 3:2, June 1971. The classic paper outlining the conditions for deadlock and how you might
go about dealing with it. There are certainly some earlier papers on this topic; see the references within
this paper for details.
[D64] “Een algorithme ter voorkoming van de dodelijke omarming” by Edsger Dijkstra. 1964.
Available: http://www.cs.utexas.edu/users/EWD/ewd01xx/EWD108.PDF. Indeed, not only
did Dijkstra come up with a number of solutions to the deadlock problem, he was the first to note its
existence, at least in written form. However, he called it the “deadly embrace”, which (thankfully) did
not catch on.
[GD02] “MapReduce: Simplified Data Processing on Large Clusters” by Sanjay Ghemawhat,
Jeff Dean. OSDI ’04, San Francisco, CA, October 2004. The MapReduce paper ushered in the era of
large-scale data processing, and proposes a framework for performing such computations on clusters of
generally unreliable machines.
[H01] “A Pragmatic Implementation of Non-blocking Linked-lists” by Tim Harris. Interna-
tional Conference on Distributed Computing (DISC), 2001. A relatively modern example of the
difficulties of building something as simple as a concurrent linked list without locks.
[H91] “Wait-free Synchronization” by Maurice Herlihy . ACM TOPLAS, 13:1, January 1991.
Herlihy’s work pioneers the ideas behind wait-free approaches to writing concurrent programs. These
approaches tend to be complex and hard, often more difficult than using locks correctly, probably limiting
their success in the real world.
[H93] “A Methodology for Implementing Highly Concurrent Data Objects” by Maurice Her-
lihy. ACM TOPLAS, 15:5, November 1993. A nice overview of lock-free and wait-free structures.
Both approaches eschew locks, but wait-free approaches are harder to realize, as they try to ensure than
any operation on a concurrent structure will terminate in a finite number of steps (e.g., no unbounded
looping).
[J+08] “Deadlock Immunity: Enabling Systems To Defend Against Deadlocks” by Horatiu
Jula, Daniel Tralamazza, Cristian Zamfir, George Candea. OSDI ’08, San Diego, CA, December
2008. An excellent recent paper on deadlocks and how to avoid getting caught in the same ones over
and over again in a particular system.
[K81] “Soul of a New Machine” by Tracy Kidder. Backbay Books, 2000 (reprint of 1980 ver-
sion). A must-read for any systems builder or engineer, detailing the early days of how a team inside
Data General (DG), led by Tom West, worked to produce a “new machine.” Kidder’s other books are
also excellent, including “Mountains beyond Mountains.” Or maybe you don’t agree with us, comma?
[K87] “Deadlock Detection in Distributed Databases” by Edgar Knapp. ACM Computing Sur-
veys, 19:4, December 1987. An excellent overview of deadlock detection in distributed database sys-
tems. Also points to a number of other related works, and thus is a good place to start your reading.
[L+08] “Learning from Mistakes — A Comprehensive Study on Real World Concurrency Bug
Characteristics” by Shan Lu, Soyeon Park, Eunsoo Seo, Yuanyuan Zhou. ASPLOS ’08, March
2008, Seattle, Washington. The first in-depth study of concurrency bugs in real software, and the basis
for this chapter. Look at Y.Y. Zhou’s or Shan Lu’s web pages for many more interesting papers on bugs.
[T+94] “Linux File Memory Map Code” by Linus Torvalds and many others. Available online
at: http://lxr.free-electrons.com/source/mm/filemap.c. Thanks to Michael Wal-
fish (NYU) for pointing out this precious example. The real world, as you can see in this file, can be a
bit more complex than the simple clarity found in textbooks…
c© 2008–19, ARPACI-DUSSEAU
THREE
EASY
PIECES
16 COMMON CONCURRENCY PROBLEMS
Homework (Code)
This homework lets you explore some real code that deadlocks (or
avoids deadlock). The different versions of code correspond to different
approaches to avoiding deadlock in a simplified vector add() routine.
See the README for details on these programs and their common sub-
strate.
Questions
1. First let’s make sure you understand how the programs generally work, and
some of the key options. Study the code in vector-deadlock.c, as well
as in main-common.c and related files.
Now, run ./vector-deadlock -n 2 -l 1 -v, which instantiates two
threads (-n 2), each of which does one vector add (-l 1), and does so in
verbose mode (-v). Make sure you understand the output. How does the
output change from run to run?
2. Now add the -d flag, and change the number of loops (-l) from 1 to higher
numbers. What happens? Does the code (always) deadlock?
3. How does changing the number of threads (-n) change the outcome of the
program? Are there any values of -n that ensure no deadlock occurs?
4. Now examine the code in vector-global-order.c. First, make sure you
understand what the code is trying to do; do you understand why the code
avoids deadlock? Also, why is there a special case in this vector add()
routine when the source and destination vectors are the same?
5. Now run the code with the following flags: -t -n 2 -l 100000 -d.
How long does the code take to complete? How does the total time change
when you increase the number of loops, or the number of threads?
6. What happens if you turn on the parallelism flag (-p)? How much would
you expect performance to change when each thread is working on adding
different vectors (which is what -p enables) versus working on the same
ones?
7. Now let’s study vector-try-wait.c. First make sure you understand
the code. Is the first call to pthread mutex trylock() really needed?
Now run the code. How fast does it run compared to the global order ap-
proach? How does the number of retries, as counted by the code, change as
the number of threads increases?
8. Now let’s look at vector-avoid-hold-and-wait.c. What is the main
problem with this approach? How does its performance compare to the
other versions, when running both with -p and without it?
9. Finally, let’s look at vector-nolock.c. This version doesn’t use locks at
all; does it provide the exact same semantics as the other versions? Why or
why not?
10. Now compare its performance to the other versions, both when threads are
working on the same two vectors (no -p) and when each thread is working
on separate vectors (-p). How does this no-lock version perform?
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