#FluidX3D #CFD v3.2 is out! I've implemented the much requested #GPU summation for object force/torque; it's ~20x faster than #CPU #multithreading.
Horizontal sum in #OpenCL was a nice exercise - first local memory reduction and then hardware-supported atomic floating-point add in VRAM, in a single-stage kernel. Hammering atomics isn't too bad as each of the ~10-340 workgroups dispatched at a time does only a single atomic add.
Also improved volumetric #raytracing!
https://github.com/ProjectPhysX/FluidX3D/releases/tag/v3.2

FLAC 1.5 Unleashes Multi-Threaded Encoding: A Game Changer for Audio Processing

The release of FLAC 1.5 marks a significant advancement in audio processing technology, introducing multi-threaded encoding that harnesses the power of modern multi-core CPUs. This update not only enh...

https://news.lavx.hu/article/flac-1-5-unleashes-multi-threaded-encoding-a-game-changer-for-audio-processing

Remember when I mentioned we had ported our #fire propagation #cellularAutomaton from #Python to #Julia, gaining performance and the ability to parallelize more easily and efficiently?

A couple of days ago we had to run another big batch of simulations and while things progressed well at the beginning, we saw the parallel threads apparently hanging one by one until the whole process sat there doing who know what.

Our initial suspicion was that we had come across some weird #JuliaLang issue with #multithreading, which seemed to be confirmed by some posts we found on the Julia forums. We tried the workarounds suggested there, to no avail. We tried a different number of threads, and this led to the hang occurring after a different percent completion. We tried restarting the simulations skipping the ones already done. It always got stuck at the same place (for the same number of threads).

So, what was the problem?

1/n

Unraveling Threads: The Hidden Mechanics of Multithreading in Computing

Ever wondered what threads really are in the realm of programming? This deep dive reveals the intricate relationship between processors and threads, demystifying how your computer runs multiple tasks ...

https://news.lavx.hu/article/unraveling-threads-the-hidden-mechanics-of-multithreading-in-computing

Multithreading can lead to deadlocks. Do one thing at a time, you are not the JVM.

December 13
#ZenDevAdvent #java #programming #multithreading

@stefano @ricardo #multithreading

Multithreaded CLI developers: let your users configure the number of threads.

Entire classes of use cases are hiding inside that will make your life easier as a dev -- and threads=1 is usually not hard to add.

One example: if your multithreaded tool works significantly faster on a single file when I force your tool to just use a single thread and parallelize it with parallel --pipepart --block instead, then either:

1. you might decide to develop sharding the I/O of the physical file yourself, or

2. you might consciously decide to not develop it, and leave that complexity to parallel (which is fine!)

But if your tool has no threads=N option, I have no workaround.

Configurable thread count lets me optimize in the meantime (or instead).

Sadhana Srinivasan: The Gilectomy: Past, Present and Future @ PyCon Ireland 2023

https://youtu.be/bC5pdoOXXoc

In Python, multithreading has historically faced challenges due to the Global Interpreter Lock (GIL). However, after years of ongoing efforts, the community has achieved a breakthrough with the acceptance of PEP 703.
This talk will delve into the GIL, its origins, and the reasons for its removal.