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I’m very new to GPGPU and Programming. I’m interested to know if statistical hypothesis testing like one-sample Kolmogorov-Smirnov test (K–S test) and Levene’s test could be implemented in GPGPU (SIMD) using CUDA? If so what will be the limitations?
I just read web definitions about these tests, but, if I understood correctly, they can be properly accelerated by the kind of parallelism expressed by SIMD (in particular as implemented by CUDA).
In K-S test, one has to compute the difference between a function and an estimate on N samples, then take the maximum difference. In other words, one has to perform the same operation on N different values, which is exactly SIMD (single instruction, multiple data).
In Levene’s test, there is again the same difference, square and multiplication over N different values.
What SIMD can do is a sort of FOR statement over N value sets, provided that the iterations are independent from each other. Thus, in CUDA for example, the compiler can allocate the iterations to the processing elements of the graphic device, so that, executing in parallel, the FOR loop is run for all the data in the time of a single iteration.
The CUDA toolkit provides a specific C/C++ compiler (NVCC) where special instructions are dispatched to the GPGPU rather than to the CPU, therefore distributed to its parallel processing elements.