# Thread- and process-based parallelisms in Transducers.jl (+ some news)

**URL:** <https://discourse.julialang.org/t/thread-and-process-based-parallelisms-in-transducers-jl-some-news/32285>\
**Category:** Package Announcements\
**Tags:** parallel, multithreading, distributed\
**Created:** [December 15, 2019, 2:39am UTC](https://discourse.julialang.org/t/thread-and-process-based-parallelisms-in-transducers-jl-some-news/32285 "2019-12-15T02:39:01Z")\
**Posts on this page:** 1\
**Showing post:** 3

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**Author:** ![tkf](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/tkf/32/17635_2.png) [@tkf](https://discourse.julialang.org/u/tkf)\
**Post date:** [December 16, 2019, 3:10pm UTC](https://discourse.julialang.org/t/thread-and-process-based-parallelisms-in-transducers-jl-some-news/32285/3 "2019-12-16T15:10:58Z")

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I forgot to mention this, but [Example for the Depth first multithread implementation performance gain as a motivation](https://discourse.julialang.org/t/example-for-the-depth-first-multithread-implementation-performance-gain-as-a-motivation/32328) reminded me that the early termination feature depends on that Julia scheduler being _depth-first_. The computed result is deterministic and scheduler independent. However, the depth-first scheduling makes it possible to terminate as early as possible by writing the reduction in divide-and-conquer approach. It makes the implementation very straightforward, if not trivial. A big thanks to Julia dev team!

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