HPX VS NCCL

https://github.com/STEllAR-GROUP/hpx Modern C++ concepts incorporated in a threading library. Lots of useful techniques used in there and we are trying to keep our code base very tidy. Feel free to chime in our libera channel #ste||ar if you have any questions.

https://github.com/STEllAR-GROUP/hpx Welcoming community + we have been part of GSoC for 4-5 years now so feel free to apply there when it opens ;)

You definitely do not want to learn Boost, trust me. The cudatoolkit is fine, HPX is great, so are Dask, and Ray. I do not recommend MPI unless those computers you have use InfiniBand.

I found this interesting library that might fit your goal.

I personally like the ideas in Parallelism v2 TS, which is available in for libstdc++ 11 onwards. The reference implementation is a library named Vc (afaik Vc is the most popular SIMD library for C++), and this has also been implemented in recent versions of HPX.

I am aware of DAGs of task based threading library like Taskflow and HPX however the benefit they have is not obvious to me, as the following sequential section depends on the parallel part being completed fully. If you want to suggest elaboration on the benefits of this approach would be welcome.

Github: https://github.com/STEllAR-GROUP/hpx

We're working on something similar, but based on sender/receiver in HPX (a lightweight threading runtime) and DLA-Future (distributed linear algebra currently based on (HPX) futures; based on sender/receiver in the future). With senders-as-awaitables this would also get you coroutine support for asynchronous MPI calls for free. We don't have that yet, but it's planned. In the meantime libunifex should be able to fill in the gaps.

% rm -rf /tmp/nccl ; git clone --recursive https://github.com/NVIDIA/nccl.git ; cd nccl ; git grep MPI Cloning into 'nccl'... remote: Enumerating objects: 2769, done. remote: Counting objects: 100% (336/336), done. remote: Compressing objects: 100% (140/140), done. remote: Total 2769 (delta 201), reused 287 (delta 196), pack-reused 2433 Receiving objects: 100% (2769/2769), 3.04 MiB | 3.37 MiB/s, done. Resolving deltas: 100% (1820/1820), done. README.md:NCCL (pronounced "Nickel") is a stand-alone library of standard communication routines for GPUs, implementing all-reduce, all-gather, reduce, broadcast, reduce-scatter, as well as any send/receive based communication pattern. It has been optimized to achieve high bandwidth on platforms using PCIe, NVLink, NVswitch, as well as networking using InfiniBand Verbs or TCP/IP sockets. NCCL supports an arbitrary number of GPUs installed in a single node or across multiple nodes, and can be used in either single- or multi-process (e.g., MPI) applications. src/collectives/broadcast.cc:/* Deprecated original "in place" function, similar to MPI */

If it's rendering, you don't really need SLI, you need to install NCCL so that GPUs memory can be pooled: https://github.com/NVIDIA/nccl

backends from native torch distributed configuration: nccl, gloo, mpi.