What an honor to start the #IWOCL conference with my keynote talk! Nowhere else you get to talk to so many #OpenCL and #SYCL experts in one room! I shared some updates on my #FluidX3D #CFD solver, how I optimized it at the smallest level of a single grid cell, to scale it up on the largest #Intel #Xeon6 #HPC systems that provide more memory capacity than any #GPU server. 
Even now, Thrust as a dependency is one of the main reason why we have a #CUDA backend, a #HIP / #ROCm backend and a pure #CPU backend in #GPUSPH, but not a #SYCL or #OneAPI backend (which would allow us to extend hardware support to #Intel GPUs). <https://doi.org/10.1002/cpe.8313>
This is also one of the reason why we implemented our own #BLAS routines when we introduced the semi-implicit integrator. A side-effect of this choice is that it allowed us to develop the improved #BiCGSTAB that I've had the opportunity to mention before <https://doi.org/10.1016/j.jcp.2022.111413>. Sometimes I do wonder if it would be appropriate to “excorporate” it into its own library for general use, since it's something that would benefit others. OTOH, this one was developed specifically for GPUSPH and it's tightly integrated with the rest of it (including its support for multi-GPU), and refactoring to turn it into a library like cuBLAS is
a. too much effort
b. probably not worth it.
Again, following @eniko's original thread, it's really not that hard to roll your own, and probably less time consuming than trying to wrangle your way through an API that may or may not fit your needs.
6/
One of the nice things of the refactoring that I had to do to introduce CPU support is that it also allowed me to trivially had support for #AMD #HIP / #ROCm.
That, and the fact that AMD engineers have written a drop-in replacement for the Thrust library that we depend on in a couple of places. (This is also one of the things that is holding back a full #SYCL port for #GPUSPH, BTW.)
It's out, if anyone is curious
https://doi.org/10.1002/cpe.8313
This is a “how to” guide. #GPUSPH, as the name suggests, was designed from the ground up to run on #GPU (w/ #CUDA, for historical reasons). We wrote a CPU version a long time ago for a publication that required a comparison, but it was never maintained. In 2021, I finally took the plunge, and taking inspiration from #SYCL, adapted the device code in functor form, so that it could be “trivially” compiled for CPU as well.
Howdy all - registrations are still open for the first oneAPI DevSummit hosted by the UXL Foundation! Learn about GPGPU programming, oneAPI and how companies are coalescing around #oneapi / #sycl
https://linuxfoundation.regfox.com/oneapiuxldevsummit2024
Registration will closeat 5pm today. The DevSummit will start at 8pm PT or 8:30am IST. See you there!
Two very busy and exhausting days at the @thekhronosgroup face-to-face planning out some awesome #SYCL developments for the coming year. Keep an eye on the news feed at https://khronos.org/sycl for more details soon...
Hit the Van Sickle (#SYCL?) trail today, always a good time.
This is what #CUDA is, a prison from which there seems to be no possibility of escaping, and also the fault of developers who rely on this, forcing anyone to necessarily buy these cards, I hope this nightmare ends soon, I'm done with having to buy Nvidia products. #NVIDIA #SYCL #intel #OneApi #opensource
