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Exascale applications will exploit a massive amount of parallelism.
The amount of parallelism, however, depends on the number of sink particles.
Their popularity stems from the fact that they can be reprogrammed to implement any function, with any amount of parallelism.
These variations transform the simple (O(N^{2})) shared time-step implementation in a complex method, where the amount of parallelism can differ per time-step.
We evaluated those schemes on the basis of global memory requirements, shared memory requirements, memory access patterns and the amount of parallelism.
However, achieving this goal is a challenging process, as most CT applications have not been designed for exploiting the amount of parallelism existing in these architectures.
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Due to the large amount of potential parallelism, resource management is a critical issue in multithreaded execution.
Due to the large amount of potential parallelism, resource management is a critical issue in multi-threaded architecture.
As a consequence the amount of available parallelism in this algorithm is very diverse, and depends directly on the number of active sink particles.
Higher performance is achieved for lower II; increasing II will reduce the amount of used parallelism, however, this makes finding a valid schedule easier [32].
First, when considering porting an application to an HPRC system, it is important to consider whether the amount of available parallelism is sufficient to provide good speedup.
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