SKIRT: Hybrid parallelization of radiative transfer simulations

We describe the design, implementation and performance of the new hybrid parallelization scheme in our Monte Carlo radiative transfer code SKIRT, which has been used extensively for modelling the continuum radiation of dusty astrophysical systems including late-type galaxies and dusty tori. The hybr...

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Vydáno v:	Astronomy and computing Ročník 20; s. 16 - 33
Hlavní autoři:	Verstocken, S., Van De Putte, D., Camps, P., Baes, M.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.07.2017
Témata:	Concurrency control — software and its engineering Multiprocessing / multiprogramming / multitasking — software and its engineering Multithreading — software and its engineering Numerical — software and its engineering Process synchronization Radiative transfer — methods Radiative transfer — methods Concurrency control — software and its engineering Multithreading — software and its engineering Multiprocessing / multiprogramming / multitasking — software and its engineering Numerical — software and its engineering Process synchronization
ISSN:	2213-1337, 2213-1345
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Shrnutí:	We describe the design, implementation and performance of the new hybrid parallelization scheme in our Monte Carlo radiative transfer code SKIRT, which has been used extensively for modelling the continuum radiation of dusty astrophysical systems including late-type galaxies and dusty tori. The hybrid scheme combines distributed memory parallelization, using the standard Message Passing Interface (MPI) to communicate between processes, and shared memory parallelization, providing multiple execution threads within each process to avoid duplication of data structures. The synchronization between multiple threads is accomplished through atomic operations without high-level locking (also called lock-free programming). This improves the scaling behaviour of the code and substantially simplifies the implementation of the hybrid scheme. The result is an extremely flexible solution that adjusts to the number of available nodes, processors and memory, and consequently performs well on a wide variety of computing architectures.
ISSN:	2213-1337 2213-1345
DOI:	10.1016/j.ascom.2017.05.003