NCCL VS Taskflow

% 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.

For parallel programming nowadays, personally I reach for C++ Taskflow when I really care about performance, or a mix of core.async and running multiple load balanced instances when I’m doing more traditional web backend stuff in Clojure.

Also, you can take a look to good libraries. The problem is that very often libraries are heavily templated, so It could be hard. For example, I like the style of the Taskflow library, I think is very clear, is relatively small, while makes use of more advanced techniques: https://github.com/taskflow/taskflow

Cool. Thanks! How does it compare to taskflow?

I've not seen yet, but it's been a bit since I looked last, any evidence of being able to build a computation graph and "save" it to re-run on new inputs. Something like https://github.com/taskflow/taskflow

It seems you're describing something a generic parallel task framework. Check taskflow for a production ready example https://github.com/taskflow/taskflow/blob/master/

- https://github.com/taskflow/taskflow (I recommend to learn it first since its API and documentation are excellent)

If you want some sort of "job" system, where you submit items to a some sort of queue to be processed in parallel, try searching for a thread pool - there isn't one in the standard library, but there's about a million implementations online. There are more complicated versions of that idea, that describe computation as a directed acyclic graph, such as taskflow.

The tooling decisions affect engine design though. For example if you want to have visual representation of job graph as it happened in specific frame of interest you need to pass the information around about job relationships and output it to a tool of choice. For example see https://github.com/taskflow/taskflow

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.