Use offloading to the GPU and SIMD hybrid parallelization to maximize the parallel performance of the nested loop.
Certain nested loops may benefit from using a hybrid parallelization approach by offloading them to the GPU while requesting vectorization for the inner loop. In this way, the use of modern hardware is maximized by offloading work to the GPU and at the same time exploiting the vectorization-like GPU capabilities. For instance, NVIDIA GPUs organize their processing units in a hierarchical structure whose smaller blocks are groups of 32 units called warps which execute the same instruction. Every time one of these GPUs execute an instruction, at least 32 of its processing units are executing it in parallel. This is analogous to a SIMD processor with 32-element lanes and can be exploited in the same fashion.
Parallelize the nested loops using offloading for the outer loop and SIMD vectorization for the inner loop. You can do so for OpenMP by invoking:
pwdirectives --omp offload+simd <foo.c:5> -o foo-hybrid.c
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