The Julia developers are pleased to announce that Julia v1.7.0 is now in beta, and binaries for v1.7.0-beta2 are available now. You can get them at https://julialang.org/downloads in the “upcoming release” section for macOS (x86-64), FreeBSD (x86-64), Windows (32-, 64-bit), and glibc Linux (i686, x8…

Wow! I did a quick test and this works so much faster and better than previous releases. No more slow jump-ahead for the twister rng and no need to assign a task the rng explicitly to guarantee reproducibility. Every Task object has a local random number generator state, providing reproducible (s…

df[(remembering .> parens) .& (was .< crucial)] #v1.6 df[this .> is .&& absolutely .< brilliant] #v1.7 Thank you @mbauman for #39594 .

Yeah, this is a pretty huge release for multithreaded PRNG usage. Courtesy of chethega (I should probably know his Discourse user name, but I don’t) initially and pushed over the finish line by @jeff.bezanson , with much assistance from @rfourquet . The down side is that the default RNG now produces a…

Personally I think it is totally worth it. I see my c# devs write so much code to deal with this situation. I think I may have found a bug when running the same code on a single threaded instance in Julia and an instance with 4 cores. I would think since each task has a local random number genera…

Different RNGs per thread means different random numbers per thread - the collection is partitioned and subsequently each thread fills its respective section with random numbers from its RNG.

[image] stephenll: Personally I think it is totally worth it. Glad to hear it! We thought so too. We’ve also been warning people for a long time now that the default PRNG sequence should not be relied on to be stable, so hopefully those warnings have helped a bit. My guess is that there are p…

I thought because the Tasks where given their own local random number generator state in 1.7 beta, that the number of threads wouldn’t matter.

The number of tasks that @threads generates depends on the number of threads. If you did something like this instead, you get the expected result: for i in 1:N Threads.@spawn x[i] = rand() end This produces the same values no matter the number of threads. This is not what the @threads macro do…

[image] StefanKarpinski: Courtesy of chethega (I should probably know his Discourse user name, but I don’t) @foobar_lv2 ?

Ah yes! I forgot that was his user name here. Sorry, @foobar_lv2 !

Julia v1.7.0-beta2 is now available

Announcements

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Random numbers and threads General Usage question , multithreading , random	30	4155	March 29, 2022
Is this pattern still thread safe in Julia 1.12? General Usage question , multithreading	14	681	October 29, 2025
How to run multiple threads, all using the same sequence of pseudorandom numbers, in Julia? General Usage multithreading , random	9	1168	August 30, 2021
A stable RNG for tests: request for feed-back Package Announcements	24	2242	August 22, 2021
Multithreaded random: different behaviour in Julia >= 1.5 (not just different stream) General Usage question , multithreading , random	6	615	March 23, 2021

Julia v1.7.0-beta2 is now available

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