wtf is nccl?

NCCL (pronounced "Nickel") is a library that helps multiple graphics cards (GPUs) talk to each other efficiently. When you're running heavy computations across several GPUs at once, like training a large AI model, the GPUs constantly need to share and combine their data. This library makes those data transfers fast, so the GPUs spend less time waiting on each other and more time doing actual work. At a high level, NCCL provides a set of standard communication operations that you'd expect in distributed computing: combining data from all GPUs into one result (all-reduce), gathering data from everyone (all-gather), broadcasting one GPU's data to all the others, and a few more. It uses "ring algorithms," which means data gets passed around the GPUs in a circle, each one adding its piece along the way, until everyone has what they need. This approach is tuned for maximum throughput, so it moves data as fast as the hardware allows. The main audience is developers building multi-GPU applications, think AI researchers training models across four or eight GPUs, or anyone running large-scale numerical simulations. If you're working with a single GPU, you don't need this. But the moment you're splitting work across multiple GPUs and need them to synchronize results, NCCL is the plumbing that makes that practical. It supports up to eight GPUs and works in both single-process and multi-process setups (like when you're using MPI, a common standard for parallel computing). One notable tradeoff: because it's optimized for throughput, small individual operations aren't the sweet spot. The documentation recommends batching small operations into larger ones whenever possible, so you get the most out of the available bandwidth. It also requires NVIDIA Kepler-era GPUs or newer, and at least CUDA 7.0, so it's specifically an NVIDIA toolkit rather than something that works across different GPU brands.