NCCL (pronounced "Nickel") is a stand-alone library of standard collective communication routines for GPUs, implementing all-reduce, all-gather, reduce, broadcast, and reduce-scatter. 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.
For more information on NCCL usage, please refer to the [NCCL documentation](https://docs.nvidia.com/deeplearning/sdk/nccl-developer-guide/index.html).
These operations are implemented using ring algorithms and have been optimized for throughput and latency. For best performance, small operations can be either batched into larger operations or aggregated through the API.
NCCL requires at least CUDA 7.0 and Kepler or newer GPUs. For PCIe based platforms, best performance is achieved when all GPUs are located on a common PCIe root complex, but multi-socket configurations are also supported.
Note: the official and tested builds of NCCL can be downloaded from: https://developer.nvidia.com/nccl. You can skip the following build steps if you choose to use the official builds.
By default, NCCL is compiled for all supported architectures. To accelerate the compilation and reduce the binary size, consider redefining `NVCC_GENCODE` (defined in `makefiles/common.mk`) to only include the architecture of the target platform :
```shell
$ make -j src.build NVCC_GENCODE="-gencode=arch=compute_70,code=sm_70"
