When we port software to the GPU or FPGA, testability is very important. A part of making the code testable, is getting its functionality fully under control. And you guessed already that run-time generated random numbers takes good attention. In a selection of past projects random numbers were generated on every run. Statistically the simulations were more correct, […]
Porting code that uses random numbers Read More »
In the first part of our two-part blog series, we have discussed how parallel computing applications can best use pseudo-random number generators (PRNGs) so as to benefit from parallel computing speedups, without negatively impacting the statistical properties of the random numbers generated. We have argued that index-based PRNGs (e.g., from the Random123 library), which do
Random Numbers in Parallel Computing: Generation and Reproducibility (Part 2) Read More »
Random numbers are important elements in stochastic simulations, but they also show up in machine learning and applications of Monte Carlo methods such as within computational finances, fluid dynamics and molecular dynamics. These are classical fields in high-performance computing, which StreamHPC has experience in. A common problem when porting traditional software in these fields to
Random Numbers in Parallel Computing: Generation and Reproducibility (Part 1) Read More »