The TL;DR is the implementer was able to get use GPUs to get merge sort speedups that exceeded CPUs once the number of elements being sorted was >10,000,000.
thrust::sort is an Nvidia C++ library; I am not clear whether it is related to CUDA or not actually; the article author started out with CUDA implementing a merge sort, but once it was slower than CPU the author tried thrust::sort library and was able to get a faster result in some cases. The article author did not yet try a parallel merge sort.
I would be curious if anyone knows what database engines take advantage of GPUs and see actual sort/query performance boosts and on what sized datasets. My impression is that a few engines have tried it, but the payoff is small enough that industry-wide people haven't adopted it.
thrust::sort is an Nvidia C++ library; I am not clear whether it is related to CUDA or not actually; the article author started out with CUDA implementing a merge sort, but once it was slower than CPU the author tried thrust::sort library and was able to get a faster result in some cases. The article author did not yet try a parallel merge sort.
I would be curious if anyone knows what database engines take advantage of GPUs and see actual sort/query performance boosts and on what sized datasets. My impression is that a few engines have tried it, but the payoff is small enough that industry-wide people haven't adopted it.