mersennetwister | pure JavaScript implementation of the Mersenne | Runtime Evironment library

Below is a class which uses the @Configuration annotation to create a Bean. I am slowly introducing Spring into a legacy Java application. I don't want to Spring-ify everything all at once - that would be crazy. I thought I'd start with one of the commonly used utility classes and I don't want Spring to create Beans for the three fields below (Enumerator, RandomDataGenerator, AliasMethodFactory). How do I turn off Autowiring for those fields? Spring is currently complaining that it can't find Beans for those (it's obviously trying to auto-wire them).

I am trying to "skip forward" a few realizations by using the function Future.randjump(), but it doesn't seem to behave as I expect it to. The following code gives me the desired result, where jumping forward 1 steps gives the same result as if I had called rand(rng) twice, i.e. the two println display the same number:

I have a Julia code, and I want to submit this code to a remote computing cluster via running a large number of jobs in parallel (i.e., around 10,000 jobs in parallel). The way this code works is that, the main function (call it "main.jl") calls another function (call it "generator.jl") which utilizes random numbers such as rand(Float64) and so on. I submit main.jl via a bash file, and I run N jobs in parallel by including

I am currently attempting to run my code in parallel. Part of my code (genetic algorithm) relies on the selection of random numbers. Therefore, I need to make sure that each thread used, have their own random number generator. I must also be able to replicate the results. Therefore, I have "borrowed" a function, which has the ability to set a random seed or create specific seeds for each thread.

https://discourse.julialang.org/t/how-do-i-deal-with-random-number-generation-when-multithreading/5636/2

In order to test if this was working correctly, I wanted to set the seeds equal for each thread and test to see if the same random numbers were generated. However, this was not the case!

I believe that I am missing something fundamental about the generation of the numbers. When creating the random number generators, I have used the same UInt32. I believe that they should therefore draw the same numbers, however the randjump may interfere with this. Perhaps the random number generators do not "jump" forward to the same point/state to ensure that each is far enough from each other in the cycle. Perhaps they share a state when doing it this way? Is there a way to control if the implementation is accurate? Perhaps there is a bigger error at play here?

I've been trying to figure out how to suppress the output of the last line in Jupyter Notebook with Julia. In particular, when the last line is an assignment. In Jupyter Notebook with a Python kernel, when I do