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A multilevel parallel scheme was exploited: grain level by means of multi-GPU systems, and a finer level by data parallelism.
Both a grain level (by means of multi-GPU systems) and a finer level (by data parallelism) have been exploited in the proposed solution.
We have developed a hybrid parallelization approach based on the Divide & Conquer (D&C) principle which combines the distributed, shared, and vectorial forms of parallelism in a fine grain task-based parallelism approach applied to irregular structures.
At a fine-grain level, parallelism was investigated inside the BCJR algorithm on elementary computations [3, 4].
We propose a new fine-grained parallelism strategy, a corresponding multigrid cycling stage and efficient data mapping to the many-core architecture of GPU.
Slice-level parallelism of a fine-grained load balancing can yield additional encoding speed-up compared to the tile levels.
This way a more fine-grained parallelism could be achieved.
GPUs are highly parallel programmable microprocessors, originally born to support graphics elaboration; they are used in combination with CPU as a coprocessor to speed up numerically intensive parts of code by the means of a massive fine grained parallelism.
ISAI preconditioners can be generated via an algorithm providing fine-grained parallelism, which makes them attractive for hardware with a high concurrency level.
Canneal uses fine-grained parallelism with a lock-free algorithm and a very aggressive synchronization strategy that is based on data race recovery instead of avoidance.
Also, it has the advantage of fine-grained parallelism on a loop level, in which each MPI process spawns a team of threads to occupy the multicore processors when encountering parallel sections of code using OpenMP.
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