ichbinstudent opened a new pull request, #2186: URL: https://github.com/apache/systemds/pull/2186
# Summary Adding counter based RNG which improves random quality and speed # Details The PRNG uses Philox4x64_10 to generate batches of random double values with a 64 bit randomness. The algorithm was tested for correctness by comparing its output for certain counters and keys with an existing implementation in c. This reference implementation called [openRAND](https://github.com/msu-sparta/OpenRAND) was tested by the authors using various statistical methods described [here](https://www.sciencedirect.com/science/article/pii/S2352711024001444). ## Advantages - Quality: Instead of 32 bits in java.util.Random, Philox4_64 produces 64 bits of randomness. While java.util.Random has a period of only (2^48), while the period of Philox4_64 is 2^256 - 1. - Speed: While the Java version of Philox4x64_10 is only about half as fast as java.util.Random, there is a Cuda kernel version available producing the exact same sequence of random numbers. This means that the Cuda and Java versions can be used interchangeably. If a system has support for Cuda, the kernel ist used, if not, the Java version can be used as a fallback. The kernel version is around 200 times faster than java.util.Random, and even faster, if the results are not copied to the CPU but kept in the GPU's memory. - Parallelisation: When using state based PRNGs, it is impossible to generate the same random matrix when changing the block size. With counter based PRNGs it is possible to change the block size but still compute the same random matrix by using the global index (row * row_size + col) as the counter. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: dev-unsubscr...@systemds.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
