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The efficiency of different levels of parallelism and different parallel architectures are compared with each other.
Optimizations of the simulation code at the several finer levels of parallelism that the data-parallel devices provide are described in terms of data layout, data flow and data-parallel instructions.
Our massively parallel implementation of this general scheme has three levels of parallelism, being a hybrid of the loosely coupled task-based parallelization approach and the conventional MPI +X programming model, where X is either OpenMP or OpenACC.
It is designed to be scalable in parallel degree and to provide different customizable levels of parallelism, by exploring the intrinsic parallelism of the FPGA.
Since 3D PMM is a parallel method, the resulting algorithm has two levels of parallelism.
The challenge is the development of parallel applications able to exploit in an effective way these different levels of parallelism; in particular, in this work we use MPI, OpenMP, and CUDA.
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In this section, we will describe the first level of parallelism, that is, parallelized calculation of heuristic functions in A* search.
On the other hand, processes execute in parallel according to the level of parallelism provided by the underlying operating system and hardware.
The TDMP algorithm [8, 9] is a rowwise, layered-decoding belief-propagation algorithm [10] that is readily adapted to differing levels of partially parallel computation based on the level of parallelism available in the processor.
The use of data parallel methods ensures that the techniques used within this chapter are applicable to emerging multicore and data parallel architectures that will continue to increase their level of parallelism to improve performance.
The highest level of parallelism duplicates the whole turbo decoder to process frames in parallel.
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