ITK  4.13.0
Insight Segmentation and Registration Toolkit


ITK is designed to run in multiprocessor environments. Many of ITK's filters are multithreaded. When a multithreading filter executes, it automatically divides the work amongst multiprocessors in a shared memory configuration. We call this "Filter Level Multithreading". Applications built with ITK can also manage their own execution threads. For instance, an application might use one thread for processing data and another thread for a user interface. We call this "Application Level Multithreading".

Filters may process their data in multiple threads in a shared memory configuration.

Filter Level Multithreading

A multithreaded filter provides an implementation of the ThreadedGenerateData() method (see itk::ImageSource::ThreadedGenerateData()) as opposed to the normal single threaded GenerateData() method (see itk::ImageSource::GenerateData()). A superclass of the filter will spawn several threads (usually matching the number of processors in the system) and call ThreadedGenerateData() in each thread specifying the portion of the output that a given thread is responsible for generating. For instance, on a dual processor computer, an image processing filter will spawn two threads, each processing thread will generate one half of the output image, and each thread is restricted to writing to separate portions of the output image. Note that the "entire" input and "entire" output images (i.e. what would be available normally to the GenerateData() method, see the discussion on Streaming) are available to each call of ThreadedGenerateData(). Each thread is allowed to read from anywhere in the input image but each thread can only write to its designated portion of the output image.

The output image is a single contiguous block on memory that is used for all processing threads. Each thread is informed which pixels they are responsible for producing the output values. All the threads write to this same block of memory but a given thread is only allowed to set specific pixels.

Memory Management

The GenerateData() method is responsible for allocation the output bulk data. For an image processing filter, this corresponds to calling Allocate() on the output image object. If a filter is multithreaded, then it does not provide a GenerateData() method but provides a ThreadedGenerateData() method. In this case, a superclass' GenerateData() method will allocate the output bulk data and call ThreadedGenerateData() for each thread. If a filter is not multithreaded, then it must provided its own GenerateData() method and allocate the bulk output data (for instance, calling Allocate() on an output image) itself.

Application Level Multithreading

ITK applications can be written to have multiple execution threads. This is distinct from a given filter dividing its labor across multiple execution threads. If the former, the application is responsible for spawning the separate execution threads, terminating threads, and handling all events mechanisms. (itk::MultiThreader can be used to spawn threads and terminate threads in a platform independent manner.) In the latter case, an individual filter will automatically spawn threads, execute an algorithm, and terminate the processing threads.

Care must in taken in setting up an application to have separate application level (as opposed to filter level) execution threads. Individual ITK objects are not guarenteed to be thread safe. By this we mean that a single instance of an object should only be modified by a single execution thread. You should not try to modify a single instance of an object in multiple execution threads.

ITK is designed so that different instances of the same class can be accessed in different execution threads. But multiple threads should not attempt to modify a single instance. This granularity of thread safety was chosen as a compromise between performance and flexibility. If we allow ITK objects to be modified in multiple threads then ITK would have to mutex every access to every instance variable of a class. This would severly affect performance.

Thread Safety in the Numerics Library

ITK uses a C++ wrapper around the standard NETLIB distributions ( These NETLIB distributions were converted from FORTRAN to C using the standard f2c converter ( A cursory glance at the f2c generated NETLIB C code yields the impression that the NETLIB code is not thread safe (due to COMMON blocks being translated to function scope statics). We are still investigating this matter.