Developer documentation
Version 3.0_RC3-135-g2b8e7d0c
Writing multi-threaded applications

Multi-threading allows applications to make use of all available CPU cores on modern multi-core systems, providing an immediate performance boost. MRtrix uses the C++11 threads API to enable multi-threaded processing where possible. To facilitate development of multi-threaded applications, MRtrix includes a number of classes that make it easy for many common workflows.


In a nutshell, multi-threading refers to the concurrent execution of a number of light-weight processes (threads) that share a common memory address space. This makes it very easy for processes to process shared data, but also very easy for one process to unexpectedly modify data that other threads are currently operating on. The biggest challenge in multi-threaded programming is managing concurrent access to shared resources and avoiding race conditions. Concurrent execution can introduce strange and unexpected bugs, often difficult to reproduce, and whose symptoms are often seemingly unrelated to the original cause. This makes multi-threaded applications very difficult to debug. For this reason, the best approach to developing multi-threaded applications is to keep things simple and clearly organised, and to code with concurrency in mind - even if the class you might be writing is not designed to be used in a multi-threaded context now, it might make sense later.

The most common strategy for managing concurrent access is the concept of mutual exclusion (a.k.a. mutex). Essentially, this involves identifying any shared resource that threads will need to access, and programming access to this resource in such a way that each thread is guaranteed sole access to the resource while it operates on it. This essentially serialises access to the resource, ensuring that each operation is completed before the next thread is allowed access. For example, iterating through a set of data in a multi-threaded fashion might involve each thread locking a mutex as it fetches the next item of data to be processed from a queue. This ensures that no two threads end up with the same data item to process, that no item accidentally remains unprocessed, etc.

How to design a multi-threading application

Designing multi-threaded applications requires a different way of programming, both to achieve a simple and robust solution that is not likely to crash, and also to realise the expected performance gains. The single most important aspect in the design process is how to structure the code to clearly define which bits are 'owned' by each thread, which bits are shared across them, and implementing strategies to enforce this design.

Sharing read-only data

The approach generally used in MRtrix applications is to define a class to hold data that threads need read-only access to during processing. This class is then initialised prior to creating and launching the threads, and is passed by const-reference to the threads to prevent the threads from unintentionally modifying the data. For example:

class Shared:
Shared () {
// initialise, etc
SomeType shared_parameters;

This class might for example contain all the reconstruction parameters set by the user, and any number of potentially large structures (e.g. matrices) that will be used (but not modified) by the other threads. Once initialised, it is then passed by const-reference to the thread class's constructor:

class Thread {
Thread (const Shared& shared) :
shared (shared) {
// any per-thread initialisation
const Shared& shared;
OtherType local_variables;

This structure helps to clearly delineate what is shared across threads and what is private to each thread. Using this construct, each thread class has read-only (const) access to the shared data, and any attempt at non-const access to this data will result in a compile-time failure. This is a very good thing, since trying to debug subtle bugs introduced by race conditions can be very frustrating, and any strategy designed to catch unintended access will save many hours of exasperation further down the track.

Sharing read/write data

To allow thread-safe read/write access, the lowest-level approach is to design an additional class that encapsulates the data to be protected. This class provides methods to access data items, and manages the mutex associated with the data. For example:

class SharedRW {
Item get_next () {
// note: this is pseudo-code!
Item item = list.pop();
return item;

In this example, access to the list is protected by locking and releasing a mutex before and after reading the next item from the list, ensuring no two threads can modify the list's internal data structures concurrently (note the lock_mutex() & unlock_mutex() functions used in this example are for illustration purposes only - no such functions exist within MRtrix).

Achieving maximum performance

Ideally, using multi-threading would provide a speedup factor equivalent to the number of cores used. In practice, a number of issues might prevent this from happening. Some of these are hardware-related (e.g. hyper-threading won't give all the performance benefits of a genuine multi-core equivalent CPU), some of them are due to restrictions imposed by the algorithm to be implemented, and some are caused by poor design of the implementation. While the first two are typically unavoidable, there is a lot that can be done for the latter.

The first issue to consider is the overhead of the multi-threading calls. Each call to lock or release a mutex will take up CPU cycles due to the function call and the need for the hardware to synchronise across all CPU cores. This overhead can become significant if run within a sufficiently tight loop, where the processing itself might take up fewer CPU cycles than the mutex handling.

Another related issue is the cost of holding the mutex, in terms of its impact on other threads. If the mutex is often locked, threads will spend a significant proportion of their time waiting for it to be released. This is particularly the case if the amount of processing done while the lock is held is large in relation to the amount of work done while it is not held.

These considerations suggest that optimal performance is achieved by keeping the rate of mutex locking low, and keeping the amount of work done while the lock is held small. This can often be done using simple strategies such as passing pointers to data to be processed, so that pushing or pulling them from a mutex-protected list is quick. If the operation to be performed per item is inherenly small, these can be done in batches to reduce the amount of locking.

There are many other issues that can impact performance, such as non-optimal memory access. If each thread is trying to access large amounts of data in seemingly random locations spread all over the RAM, the CPU will probably end up spending a disproportionate amount of time fetching data and flushing the CPU cache, resulting in poor performance. When this happens, multi-threading may seem to provide no benefit, and might in fact adversely affect performance compared to the single-threaded equivalent. It helps to think hard about your code's memory access patterns to take advantage of the CPU's onboard cache and the high burst transfer rates that can be achieved when the CPU can read large chunks of contiguous data. This is even more important when operating on disk-backed data, where the cost of non-linear access can be considerable.

Multi-threading API in MRtrix

MRtrix provides a number of constructs to simplify the process of writing solid multi-threading applications. In most cases, the high-level Thread-safe image looping and Thread-safe queue frameworks will be appropriate for the particular algorithm to be implemented. If a more sophisticated implementation is required, the C++11 threads API can be used directly.

Launching threads

The MR::Thread::run() function is designed to launch and manage one or more threads. This is done by providing one or more specially-designed thread classes (or functors), whose execute() method will be invoked within the newly-created thread. Multiple instances of the same thread class can be launched by wrapping the functor within the MR::Thread::multi() call when passing it to MR::Thread::run().

If the class is to be used in multiple concurrent threads (i.e. launched using MR::Thread::multi()), the class must be copy-constructable, and any copy created in this way must be fully independent: if pointers to non-const data or other complex structures are members of the class, there is a good chance the default copy constructor will provide a copy referencing the same data as the original, which would generally lead to race conditions if these were written to during processing. Most of the classes provided in MRtrix do copy-construct appropriately for use in multi-threading (for example [Image], [Adapter]), while others do not). That said, if all class members have appropriate copy constructors, the default copy constructor should also be appropriate.

For example:

class MyThread {
void execute() {
// do stuff
void run() {
MyThread thread;
auto exec = Thread::run (thread, "my thread");
// do something else
// invoking thread will wait for other thread to finish (join)
// in the thread class's destructor - i.e. when thread goes out of scope

To run it across multiple threads, use the MR::Thread::multi() wrapper. By default, the number of threads launched is determined from the C++11 std::thread::hardware_concurrency() function, from the config file or from the command line.

void run () {
MyThread thread;
auto exec = Thread::run (Thread::multi (thread), "my threads");
// do something else, or just wait for completion.
// invoking thread will wait for all other threads to finish (join)
// in thread class's destructor - i.e. when exec goes out of scope

The ThreadedLoop

It is very common in imaging to process image voxels. The MR::ThreadedLoop() function and associated classes are designed to greatly simplify the process of creating such applications. It allows the developer to provide a simple functor class implementing the operation to be performed for each voxel, which can be passed to MR::ThreadedLoop() to be executed over the whole dataset. Refer to Thread-safe image looping documentation for more information.

The Thread::Queue

In many cases, an algorithm can be designed to process a stream of data. For example, data items are read in order from disk, and can be processed independently. The results might then need to be written back to disk in a serial fashion. The MR::Thread::Queue class is designed to facilitate this type of operation. Please refer to the Thread-safe queue for a detailed description.

There is also a convenience function to simplify the setting up of the queue structure, called MR::Thread::run_queue(). This also works with the MR::Thread::multi() wrapper where multiple parallel threads are desired. Please refer to the [Thread module documentation[(Multi-threading) for details.