checkedthreads: no race condition goes unnoticed! Simple API, automatic load balancing, Valgrind-based checking
C C++ Python
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checkedthreads is a fork-join parallelism framework for C and C++ providing:

  • Automated race detection using debugging schedulers and Valgrind-based instrumentation.
  • Automatic load balancing across the available cores.
  • A simple API - nested, cancellable parallel loops and function calls.

If you have dozens of developers working on millions of lines of multithreaded code, checkedthreads will let you painlessly parallelize the code - and then continuously ship new versions without worrying about parallelism bugs. It's based on a decade of experience in that kind of environment.


What race conditions will be found?

All of them! checkedthreads provides two verification methods:

  • Fast verification using a debugging scheduler that changes the order of events.
  • Thorough verification using Valgrind-based instrumentation that monitors memory accesses and flags accesses to data owned by another thread.

There are more details below; the upshot is that every race condition will be found if:

  • It could ever manifest on the given inputs.
  • The bug is actually a race condition :-) (What looks like a race condition but isn't a race condition? Consider using uninitialized memory returned by malloc. This is a bug regardless of parallelism. This also leads to non-deterministic results in parallel programs. But the bug is not a race condition - it's a memory initialization problem. So while checkedthreads may help find the bug, no guarantees are made - unlike with pure race conditions.)

Nice features

checkedthreads is a fork-join framework not unlike many others, such as Intel TBB, Microsoft PPL, Cilk, OpenMP or GNU libstdc++ parallel mode. How to choose a framework?

Here are some nice features of checkedthreads; you can compare other frameworks' features to this list as you shop around:

  • Pretty much guaranteed bug detection
  • Integration with other frameworks
  • Dynamic load balancing
  • A C89 and a C++11 API
  • "Free" as in "do whatever you want with it"
  • Easily portable (at least in theory)
  • Custom schedulers


  • Guaranteed bug detection is a rare promise in the world of parallel imperative programs, which have a bad reputation because of hard-to-chase bugs. The API is designed with the goal of making it possible to find all parallelism bugs that could manifest on given inputs, and tools are provided for doing so.
  • Integration with other frameworks. If your code already uses TBB or OpenMP, you can have checkedthreads rely on TBB or OpenMP to run the tasks you create with the checkedthreads API. This way, you can use checkedthreads alongside another framework without the two fighting over the machine. (Please tell if you'd like to use checkedthreads alongside another framework such as PPL.)
  • Dynamic load balancing. checkedthreads comes with its own scheduler where all tasks are put in a single queue and processed by the worker thread which is "the quickest to dequeue it". (When using TBB or OpenMP, checkedthreads tries to approximate this scheduling policy.) A single queue is not necessarily scalable to 1000 cores, but it otherwise provides optimal load balancing: work gets done as soon as someone is available to do it. The upshot is that you get nice performance on practical hardware configurations.
  • A C89 as well as a C++11 API. No compiler extensions (#pragmas, keywords, etc.) are involved. While C++11 lambdas and variadic templates are used to provide some syntactic sugar, the underlying C89 API is useable directly as well.
  • Free as in "licensed under the FreeBSD license" (the 2-clause thing, see LICENSE.txt). I'll gladly license the code under any other license if you want - I originally just said "do whatever you want with the code", but people worried that lawyers would much prefer a standard license.
  • Portability. Very little is assumed about the target platform. It is enough to have a C89 compiler and an address space shared by a bunch of threads. In fact, you don't even need "threads" as in "an OS with preemptive scheduling"; you could rather easily port checkedthreads to run on a machine without any OS. (However, currently checkedthreads is only developed and tested on Linux [Ubuntu 12]. Please tell if you have problems using it on another platform, or if you want it to be easier to build on another platform.)
  • Custom schedulers: if you prefer a different scheduling policy, you can implement a scheduler of your own - you need to implement the same 3-function interface that is used to implement the schedulers supplied together with checkedthreads.

Another nice feature, at the moment, is simplicity and small size. However, these were known to transform into complexity and large size in the past. An effort will be made to avoid that.

There are also missing features - please tell if a feature you need is missing. Making a list of everything not there is a tad hard... One biggie, currently, is concurrency. No means are provided to wait for events except for issuing a blocking call (which "steals" a thread from the underlying thread pool, so it's not any good, really). Generally concurrency is not currently a use case: checkedthreads is a framework for parallelizing computational code which does not interact much with the external world.


In a nutshell:

  • You can parallelize loops (with ct_for) and function calls (with ct_invoke).
  • Both parallel loops and function calls can be nested.
  • A parallel loop or a set of parallel function calls can be cancelled before they complete.

Examples using the C++11 API:

ctx_for(100, [&](int i) {
    ctx_for(100, [&](int j) {
        array[i][j] = i*j;

Absolutely boneheaded code, but you get the idea. i and j go from 0 to 99. Currently there's no way to specify a start other than 0 or an increment other than 1. There's also no way to control "grain size" - each index is a separately scheduled task. So a non-trivial amount of work should be done per index, or the scheduling overhead will dwarf any gains from running on several cores.

For a better example, here's parallel sorting:

template<class T>
void quicksort(T* beg, T* end) {
    if (end-beg >= MIN_PAR) {
        int piv = *beg, l = 1, r = end-beg;
        while (l < r) {
            if (beg[l] <= piv) 
                std::swap(beg[l], beg[--r]);
        std::swap(beg[--l], beg[0]);
        //sort the two parts in parallel:
            [=] { quicksort(beg, beg+l); },
            [=] { quicksort(beg+r, end); }
    else {
        std::sort(beg, end);

Not unlike ctx_for, ctx_invoke calls all the functions it's passed in parallel.

No function call scheduled by ctx_invoke, nor any iteration of ctx_for, should ever access a memory address modified by any other call/iteration - that is, they should be completely independent. Once ctx_for/invoke returns, all the memory updates done by all the iterations/function calls can be used by the caller of ctx_for/invoke.

Now a C89 example:

void set_elem(int i, void* context) {
    int* array = (int*)context;
    array[i] = i;

void example(void) {
    int array[100];
    ct_for(100, set_elem, array, 0);

That last "0" is a null pointer to a canceller - we'll get to that in a moment. Meanwhile, parallel invoke in C89:

void a(void* context) { *(int*)context = 1; }
void b(void* context) { *(int*)context = 2; }

void example(void) {
    int first, second;
    ct_task tasks[] = {
        {a, &first},
        {b, &second},
        {0, 0}
    ct_invoke(tasks, 0);

The tasks[] array should have {0,0} as its last element. Again, the "0" argument is the canceller.

Speaking of which - here's an example of actually using cancelling:

int pos_of_77 = -1;
ct_canceller* c = ct_alloc_canceller();
ctx_for(N, [&](int i) {
    if(arr[i] == 77) {
        pos_of_77 = i;
}, c);

(Again a silly piece of code doing way too little work per index, but no matter.) Notes on cancelling:

  • Everything can be cancelled: ct_for, ctx_for, ct_invoke, and ctx_invoke can all get a canceller parameter.
  • A single canceller can cancel many things: ct_cancel(c) cancels all loops and parallel calls to which c was originally passed.
  • Nested loops/calls don't automatically inherit the canceller: when a loop is cancelled, no more iterations will be scheduled - but all iterations which are already in flight will be completed. If such an iteration itself spawns tasks, then those tasks will not be canceled - unless the spawning iteration explicitly passed to the tasks it spawned the same canceller which cancelled the loop that the spawner belongs to.
  • At most one iteration/function call can write something to memory - otherwise, different results might be produced depending on timing, because cancelling is not deterministic (different iterations may be cancelled in different runs). For instance, the example above is only correct if arr[] is known to keep at most one value equal to 77.

The last thing to note is that you need, before using checkedthreads, to call ct_init() - and then call ct_fini() when you're done. ct_init gets a single argument - the environment; for example:

ct_env_var env[] = {
    {"CT_SCHED", "shuffle"},
    {"CT_RAND_REV", "1"},
    {0, 0}

You can pass 0 instead of env; if you do that, $CT_SCHED and $CT_RAND_REV will be looked up using getenv(). Similarly, if you do pass an env[], all variables not mentioned in it will be getenv()d.

The available environment variables and their meaning are discussed in the next section.

Environment variables

$CT_SCHED is the scheduler to use, and can be one of:

  • serial: run loops serially from 0 to N and call functions first to last.
  • shuffle: serial run with a pseudo-random, deterministic order of iterations and function calls.
  • valgrind: same order as shuffle, but also communicates with the Valgrind checker, telling it what's what.
  • tbb: schedule tasks using TBB's simple_partitioner with grain size of 1.
  • openmp: schedule tasks using OpenMP's #pragma omp parallel for schedule(dynamic,1).
  • pthreads (default): schedule tasks using a worker pool of pthreads and a single shared queue.

$CT_THREADS is the worker pool size (relevant for the parallel schedulers); the default is a thread per core.

$CT_VERBOSE: at 2, all indexes are printed; at 1, loops/invokes; at 0 (default), nothing is printed.

$CT_RAND_SEED: a seed for order-randomizing schedulers (shuffle & valgrind).

$CT_RAND_REV: if non-zero, order-randomizing schedulers will reverse their random index permutations. When this is useful is explained in the next section.

How race detection works

As mentioned above, there are two verification methods - a fast one and a thorough one.

The fast one is, run the program twice using two different serial schedules - a random schedule and then the same schedule, reversed:

env CT_SCHED=shuffle CT_RAND_REV=0 your-program your-arguments
env CT_SCHED=shuffle CT_RAND_REV=1 your-program your-arguments

Now compare the results of the two runs. Different results indicate a bug, because results should not be affected by scheduling order (in production, a parallel scheduler is used and it can result in things running in any of the two orders you just tried - and many other orders).

Using this method, you can run the program on many inputs (the program runs serially with the shuffle scheduler, so you can spawn a process per core to fully utilize machines used for testing). Many inputs and no result differences give you a rather high confidence that your program is correct.

However, this method has two drawbacks:

  • Some bugs go unnoticed. For instance, updating a shared accumulator from several iterations of a loop may not work with a parallel scheduler. But such updates will yield the same results under all serial schedules. So will the use of a shared temporary buffer.
  • Bugs are not pinpointed. Different results prove that there's a bug - but they don't tell you where it is.

Because of these drawbacks, a second, slower and more thorough verification method is available:

env CT_SCHED=valgrind CT_RAND_REV=0 valgrind --tool=checkedthreads your-program your-arguments
env CT_SCHED=valgrind CT_RAND_REV=1 valgrind --tool=checkedthreads your-program your-arguments

(If Valgrind says "failed to start tool 'checkedthreads'", perhaps $VALGRIND_LIB should be set to point to the right place.)

This runs Valgrind with the checkedthreads tool, which monitors every memory access. When a thread accesses a location that another thread concurrently wrote, the tool prints the offending call stack:

checkedthreads: error - thread 56 accessed 0x7FF000340 [0x7FF000340,4], owned by 55
==2919==    at 0x40202C: std::_Function_handler<void (int), main::{lambda(int)#1}>::_M_invoke(std::_Any_data const&, int) (bug.cpp:16)
==2919==    by 0x403293: ct_valgrind_for_loop (valgrind_imp.c:62)
==2919==    by 0x4031C8: ct_valgrind_for (valgrind_imp.c:82)
==2919==    by 0x40283C: ct_for (ct_api.c:177)
==2919==    by 0x401E9D: main (bug.cpp:20)

Note that there aren't any actual threads - like the run under CT_SCHED=shuffle, this run is serial. Rather, the Valgrind tool maps ct_for loop indexes and ct_invoke function calls to thread IDs, such that those IDs can fit into a single byte. So "two threads accessing the same location" means that the location was accessed from two loop indexes/function calls that could run in parallel.

This second method is slower, but it doesn't miss any bugs that could ever occur with the given inputs - and it pinpoints the bugs. So it's a good idea to run the program under Valgrind on a few inputs in case plain shuffling misses bugs. And it's also useful to run the program under Valgrind on those inputs where shuffling discovered bugs - to pinpoint those bugs.

This is all you strictly need to know to verify your code. If you want more details - for instance, if you want to be convinced that the bug coverage is indeed as thorough as claimed above - you can read a detailed explanation here.

Note that there can be no deadlocks, because there are no locks (the only way to synchronize threads is forking - the spawning of loops - or joining, the termination of loops). So there's no need for deadlock detection tools.

Building and installing

It is perhaps possible to download binaries for your platform - something I always prefer to do... It is recommended that you read the following explanations about building from sources, and then decide which parts you want to build and which parts you prefer to download in binary form.

So, after cloning/downloading sources:

cd checkedthreads

configure is a Python script producing a single output, include/checkedthreads_config.h. You can edit this file manually; all it has is #defines telling which of the optional features are enabled. Note that make then checks what's #defined in config.h and adjusts compiler flags, etc.

  • #define CT_CXX11 - enable C++11 (the C++ parts of the code are compiled with -std=c++0x).
  • #define CT_OPENMP - enable OpenMP (code is compiled with -fopenmp).
  • #define CT_TBB - enable TBB.
  • #define CT_PTHREAD - enable pthreads (code is compiled with -pthread).

./configure enables each of these features if it auto-detects that it's supported on your machine. You might then want to disable some features (even though they're supported on your machine).

At this point, you can build the libraries with make build, but plain make won't work yet because things must be manually configured to build the Valgrind tool. To do that, edit and set the following variables:

  • CT_VALGRIND_SRC_DIR: the directory with the Valgrind sources. To build the tool, you need to download, configure and make Valgrind first. The Valgrind tool was developed with Valgrind 3.8.1; the binaries seem to work with 3.7.0 as well but I didn't try to build with the 3.7.0 sources. You can download the 3.8.1 sources from here or here.
  • VALGRIND_LIB: your system's default (such as /usr/lib/valgrind), or the directory you passed to Valgrind's configure with --prefix, or some other (existing) directory if you wish. The checkedthread tool binaries are copied to this directory.
  • CT_VALGRIND_CP: sudo cp by default - can be plain cp if you have permissions to access $VALGRIND_LIB without sudo.

Note that $VALGRIND_LIB is used by the tests which make runs after building everything - make sets this environment variable for all the processes it spawns. You may need to explicitly set $VALGRIND_LIB when running valgrind --tool=checkedthreads outside make.

Also note that setting, in the shell running make, any of the environment variables mentioned in overrides their values (hence the name "").

With edited, you can build everything and run tests with:


make help will list the available make targets and options (such as make clean and make VERBOSE=1). Currently every build rebuilds everything from scratch (there's no dependency checking), which is tolerable at the current size of things.

After a successful build, you get libraries at lib/ as follows:

  • Every library is available both as a static lib.a* file a dynamic* file.
  • libcheckedthreads++ has all the enabled features.
  • libcheckedthreads has all the enabled features except those relying on C++ (the C++11 API and the TBB-based scheduler).
  • If OpenMP is enabled, libcheckedthreads++_openmp is created that has all the enabled features but only one parallel scheduler, the one based on OpenMP. libcheckedthreads_openmp is similar, except that it also doesn't use C++.
  • Similarly, if pthreads are enabled, libcheckedthreads++_pthreads and libcheckedthreads_pthreads are built.
  • Similarly, if TBB is enabled, libcheckedthreads++_tbb is built (but not libcheckedthreads_tbb, because TBB requires C++).

This variety of libraries should hopefully make it easy to link with checkedthreads in any scenario...

At this point you can "install" checkedthreads - that is, copy the files in lib/ to wherever you keep your libraries, and copy the files in include/ to wherever you keep your header files.

If you want to download binaries instead of building, the available binaries are listed here. You can choose to download just the Valgrind binaries (the slightly gnarlier thing to build) but to build the libraries from sources, for example.

Planned features

Planned features not yet avaialable:

  • Proper checking of cancelling (cancelling is only OK if at most one thread writes things)
  • Custom allocator interface (to tell the checker when memory is allocated/freed)
  • A compiler (LLVM/gcc) pass in addition to the dynamic Valgrind instrumentation
  • A Windows build and integration with PPL

Coding style

  • Everything prefixed with ct_ (ctx_ for C++ identifiers), except for struct members.
  • Globals prefixed with g_ct/g_ctx; no static variables (all are extern).
  • Indentation: 1TBS, 4 spaces per level, no hard tabs.
  • Everything is lowercase, underscore_separated. Macros mostly UPPERCASE.
  • Valgrind tool code (at valgrind/) should try to use Valgrind style.
  • Code should compile without warnings (I'd use -Werror, but different gcc versions have different warnings.)
  • Style isn't that important.

Support/contact Feel free to contact if you run into any sort of problem using checkedthreads.