Parallelism

In synthesizer we have implemented an optional model of parallelism, where distributed memory parallelism (MPI) is left in the hands of the user, while shared memory parallelism (OpenMP) can be optionally invoked at a function call level for specific, intensive computations. This allows the user to have fine-grained control over parallelism in their code, and to avoid overheads where parallelism is not required.

Shared Memory Parallelism (OpenMP)

To use OpenMP threading in Synthesizer, you need to first have OpenMP installed or an OpenMP compliant compiler on your system, and to compile the code with the appropriate flags. For more details see the installation instructions and the configuration options.

> Note: The Clang compiler on macOS does not support OpenMP, so you will either need to install OpenMP via Homebrew or use a different compiler such as GCC.

Testing OpenMP

To check that OpenMP is indeed being used in your code, you can import and use the check_openmp function.

from synthesizer import check_openmp
check_openmp()

If OpenMP has been successfully configured, this function will return True, otherwise it will return False.

Using OpenMP Threading

To avoid the pitfalls of the Python Global Interpreter Lock (GIL), we have focused on parallelisation of the C extensions used for spectra generation, integration, Line-Of-Sight (LOS) surface density calculations, imaging, and other computationally intensive tasks. Making use of these threadpools is as simple as passing the nthreads argument to the relevant function. For example, to use 4 threads when generating spectra:

galaxy.get_spectra(..., nthreads=4)

The exact same would be true for any other function that supports OpenMP threading.

galaxy.stars.get_los_column_density(..., nthreads=4)