We introduce a new relativistic astrophysics code, SpECTRE, that combines a discontinuous Galerkin method with a task-based parallelism model.
A task-based parallelism model allows efficient use of the largest supercomputers for problems with a heterogeneous workload over disparate spatial and temporal scales.
Key features of the algorithm include a task-based parallelism model that allows independent MOC tracks to be assigned to threads dynamically, ensuring load balancing, and a wide vectorizable inner loop that takes advantage of modern SIMD computer architectures.
The underlying data structure is a memory efficient de-Bruijn graph (Compeau et al., 2011), and the general parallelism model is multithreading.
Due to their intrinsic parallelism, CA models are attractive since they are suitable to be effectively and naturally implemented on parallel computers achieving also high performance.
The new hybrid algorithm is developed on a base of a multi-level parallel model that exploits several layers of parallelism of a modern hybrid supercomputer.
In Figure 9, we show the structure of the parallel model developed using the fork-join model and the nested parallelism.
We present an extension of the bulk-synchronous parallel (BSP) model to abstract and model parallelism in the presence of multiple memory hierarchies and multiple cores.
The framework supports model parallelism both within a machine (via multithreading) and across machines (via message passing), with the details of parallelism, synchronization, and communication managed by DistBelief.
Another solution that we propose and have described here may be through drawing parallelisms with other models, which is to perform what we refer as "parallel validation".
