Multithreading in Mantid


Mantid uses OpenMP in C++ to improve performance by parallelizing for loops. A tutorial devoted to the technology can be found here.

Access to the OpenMP API is via a set of macros defined in MultiThreaded.h. This accomplishes seamless fall-back to single-threaded behaviour for compilers that don’t have OpenMP available, as well as providing protection against multithreading when non-thread-safe workspaces are in use.

The recommended way to use OpenMP in an algorithm loop (typically one over the spectra in a workspace) is as follows:

PARALLEL_FOR_IF(Kernel::threadSafe(*inputWS, *outputWS))
for (int i = 0; i < numSpec; ++i)

  // .... algorithm code ....


The main work is in the first statement, which contains the instruction invoking OpenMP, but only if the workspaces given are thread-safe. Analogous macros are available for zero, 2 or 3 workspaces. Any workspace that is accessed within the loop should be included.

There is then also a set of slightly verbose interrupt instructions, which prevent exceptions escaping from a parallel region (which would otherwise cause program termination) - this includes dealing with algorithm cancellation.

If you need to use OpenMP in a way that is not covered by any of the specific macros in MultiThreaded.h , you can make use of the general purpose PRAGMA_OMP macro. This is essentially the same as using #pragma omp directly. Ideally, this should only be used if what you want to do is not already covered by the other macros.

// Dynamic scheduling allows x to be split into chunks of size 1 processed by each thread,
// but assigned to threads in no particular order.
PRAGMA_OMP(parallel for schedule(dynamic, 1))
for (int i = 0; i < x.size(); ++i) {

Note: The set of INTERRUPT macros can only be used in Mantid algorithms. The rest can be used anywhere.

Ensuring thread-safety

The first rule is this: Don’t write to shared variables. Or, if you do, protect the write with PARALLEL_CRITICAL or PARALLEL_ATOMIC calls.

// Can only be used on simple operations, uses atomic access from machine hardware.

// Can be used anywhere, but has a higher overhead.
// Can be named if two critical sections can be accessed simultaneously.
if(a > 4) {

Note that a write to a workspace data spectrum selected by the loop index is not typically a shared write (though see below).

One gotcha comes from the use of copy-on-write pointers to store the workspace data. Here’s an example:

// Get references to the x data
const auto& xIn = inputWS->x(i);
auto& xOut = outputWS->mutableX(i);

This can cause problems in the case where the input and output workspaces are the same. Although the call to outputWS->mutableX() to get a reference to the output data may look innocuous, in the case where different spectra are pointing to the same underlying data array this call will cause the array to be copied, which will invalidate the reference obtained to the input data in the previous line. The solution is to make sure the non-const calls come before the const ones (in this case by reversing the two lines).


Tasks in python can be run outside of the main GUI thread by using the classes and functions defined in

The simplest and most commonly used one is AsyncTask:

self.worker = AsyncTask(self.to_be_run, (param_1, param_2),

For more OpenMP style multithreading, there is the functionality inside

The methods you wish to run asynchronously must be inside a class that inherits from IQtAsync. You can then overwrite any of the relevant callbacks and annotate async methods with the @qt_async_task decorator.

class DoesAsyncThings(IQtAsync):
    def __init__(self):

    def finished_cb_slot(self) -> None:

    def do_async(self):

Note: These methods are only useful for stopping mantid from hanging while something else is processing. Due to the nature of the Global Interpreter Lock (GIL), it is not possible to run concurrent threads in python. For heavy lifting that would require multithreading you should use C++ instead.