Minwise hashing  is a standard technique for efficient set similarity estimation in the context of search. The recent work of b-bit minwise hashing  provided a substantial improvement by storing only the lowest b bits of each hashed value. Both minwise hashing and b-bit minwise hashing require an expensive preprocessing step for applying κ (e.g., κ = 500) permutations on the entire data in order to compute κ minimal values as the hashed data. In this paper, we developed a parallelization scheme using GPUs, which reduced the processing time by a factor of 20 ∼ 80. Reducing the preprocessing time is highly beneficial in practice, for example, for duplicate web page detection (where minwise hashing is a major step in the crawling pipeline) or for increasing the testing speed of online classifiers (when the test data are not preprocessed). Copyright is held by the author/owner(s).