Naive implementation of mt_rand using pyopencl and pycuda

Numpy and one of Pycuda or pyopencl should me installed to run this.

For pycuda you should run

For any opencl compatible device you should also install opencl package for your device (AMD/NVIDIA/Intel/etc.)

For cuda - you should install nvcc compiler with headers

One can run this implementation with start_ocl.sh and start_cuda.sh. This scripts will also produce profiling information of execution

PyOpencl implementation will try to run on first device it would found. I don't know for now how to fix it to run only on GPU. If you would run it from shell - there would be some info regarding this

$ grep SIZE config.py
SIZE = 16384
$ ./start_ocl.sh
$ tail -n 2 opencl_profile_0.log
method=[ mt_brute ] gputime=[ 72.640 ] cputime=[ 4.000 ] occupancy=[ 0.667 ]
method=[ memcpyDtoHasync ] gputime=[ 12.512 ] cputime=[ 8.000 ]

$ ./start_cuda.sh
$ tail -n 2 cuda_profile_0.log
method=[ mt_brute ] gputime=[ 68.928 ] cputime=[ 6.000 ] occupancy=[ 0.667 ]
method=[ memcpyDtoH ] gputime=[ 10.720 ] cputime=[ 88.000 ]


72.640+12.51 microseconds for 2^14 hashes = 1.9 * 10^8 seeds

OR

22 seconds for all 2^32 combinations