AWS OFI NCCL

AWS OFI NCCL is a plug-in which enables EC2 developers to use libfabric as a network provider while running NVIDIA's NCCL based applications.

Overview

Machine learning frameworks running on top of NVIDIA GPUs use a library called NCCL which provides standard collective communication routines for an arbitrary number of GPUs installed across single or multiple nodes.

This project implements a plug-in which maps NCCLs connection-oriented transport APIs to libfabric's connection-less reliable interface. This allows NCCL applications to take benefit of libfabric's transport layer services like reliable message support and operating system bypass.

Requirements

The plug-in currently supports the following distributions:

• Amazon Linux
• Amazon Linux 2
• Redhat Enterprise Linux 7 and 8
• Ubuntu 18.04 and 20.04 LTS
• CentOS 7 and 8

It requires Libfabric, NCCL, HWLOC, and (if you want to run tests) an MPI Implementation. Please see the Release notes for information on version compatibility. We recommend using the distribution version of hwloc, which can be installed with yum install hwloc-devel on many RPM based distributions and apt install libhwloc-dev on many DPKG based distibutions.

Libfabric supports various providers. The plug-in can choose only those which support the following features as defined in the libfabric API documentation.

• Tagged messaging (FI_TAGGED, FI_MSG)
• Data transfer context structures (FI_CONTEXT)
• Reliable datagram endpoints (FI_EP_RDM)
• Send after Send ordering semantics (FI_ORDER_SAS)
• Communication with remote endpoints (FI_REMOTE_COMM)

For GPUDirect RDMA support, it requires these additional features from libfabric providers. If these are not supported by any provider on system, plug-in turns off GPUDirect RDMA support.

• Transfers to/from device memory (FI_HMEM)
• Remote memory operations (FI_RMA, FI_READ)

For multi-rail support, it requires FI_WRITE in addition to FI_READ.

Getting Started

Dependencies

aws-ofi-nccl requires a working installation of libfabric. You can find the instructions for installing libfabric at libfabric installation.

Build Instructions

We recommend that most users start with a release tarball available on the GitHub Release Page. The plugin uses GNU autotools for its build system. You can build it as follows:

$ ./configure
$ make
$ sudo make install

If you want to install the plugin in a custom path, use the --prefix configure flag to provide the path. You can also point the build to custom dependencies with the following flags:

  --with-libfabric=PATH   Path to non-standard libfabric installation
  --with-cuda=PATH        Path to non-standard CUDA installation
  --with-mpi=PATH         Path to non-standard MPI installation
  --with-hwloc=PATH       Path to non-standard HWLOC installation

By default, the configure script attempts to auto-detect whether it is running on an AWS EC2 instance, and if so enables AWS-specific optimizations. These optimizations can be enabled regardless of build machine with the following config option:

  --enable-platform-aws   Enable AWS-specific configuration and optimizations.
                          (default: Enabled if on EC2 instance)

To enable trace messages for debugging (disabled by default), use the following config option:

   --enable-trace         Enable printing trace messages

LTTNG tracing is documented in the doc/tracing.md file.

To enable LTTNG tracing, use the following configuration option:

  --with-lttng=PATH       Path to LTTNG installation

By default, tests are built. To disable building tests, use the following config option:

   --disable-tests        Disable build of tests.

Plugin Configurations

Similar to NCCL or Libfabric, the plugin dynamically loads CUDA dependencies at runtime, specifically libcudart.so. Like NCCL and Libfabric, the plugin does not find CUDA libraries with the CUDA_HOME environment variable. dlopen() will use the LD_LIBRARY_PATH environment variable and then your system's default search path to find libcudart.so. We do this to match NCCL and Libfabric behaviors so that all three components find the same CUDA installation.

The plugin allows to configure the following variables at run-time according to your environment.

Parameter	Description	Type	Accepted Value
OFI_NCCL_USE_IPV6_TCP	Allow using endpoints with IPv6 addressing format for TCP provider. Users can specify to use a preferred libfabric provider with `FI_PROVIDER` environment variable.	Boolean	0/1 (Default: 0)
OFI_NCCL_TCP_EXCLUDE_IF	List of interface names to be filtered out for TCP provider. Users can specify to use a preferred libfabric provider with `FI_PROVIDER` environment variable.	String	Comma-separated list of interface names (Default: "lo,docker0")
OFI_NCCL_GDR_FLUSH_DISABLE	Disable flush operation when using GPUDirect.	Boolean	0/1 (Default: 0)
OFI_NCCL_NIC_DUP_CONNS	Set number of NIC connections. This is used to increase hardware utilization. Applicable for P3Dn when using less number of GPUs than 8..	Integer	x, to set x number of connections. Only overridden for greater than 0 values (Default: 0)
OFI_NCCL_CUDA_FLUSH_ENABLE	When using GPUDirect use the cudaDeviceFlushGPUDirectRDMAWrites to enforce data consistency at the receiving GPU. Requires CUDA 11.3 or later. Note that this function only provides a GPU memory fence and requires that data has already been delivered to GPU memory. Some networks and PCIe configurations require an additional network-level flush that is not provided by this option.	Boolean	0/1 (Default: 0)
OFI_NCCL_CQ_READ_COUNT	Adjust the maximum number of completion entries that will be read in a single Libfabric polling loop. In general, users should not have to adjust this value. An array of completion queue entry structures is created on the stack, so large (over 16-32) values of this parameter may cause stack overflows.	Integer	Default: 4
OFI_NCCL_PROTOCOL	Protocol to use for implementing send/recv operations. Default is `SENDRECV`, which uses the Libfabric tagged send/recv interface. This implementation will give the best performance on hardware that implements tagged sends natively, and likely most Libfabric implementations that include an eager send optimization for GPU buffers. The other valid option is `RDMA`, which implements a sender-managed receive queue using RDMA write operations and supports multi-rail channels per GPU. The `RDMA` protocol is likely to work better than `SENDRECV` on networks that do not have an eager optimization or that have multiple NICs per GPU.	String	Default: SENDRECV
OFI_NCCL_TOPO_FILE_WRITE_ENABLE	When enabled and RDMA communication protocol is used, write NCCL topology file and set environment variable `NCCL_TOPO_FILE`. By default, plugin writes the NCCL topology file to a unique temporary file using file path template `/tmp/aws-ofi-nccl-topo-XXXXXX` and the file is deleted at normal process termination. See environment variable `OFI_NCCL_TOPO_FILE_TEMPLATE` to control the file destination.	Boolean	0/1 (Default: 0)
OFI_NCCL_TOPO_FILE_TEMPLATE	Template path to a file to control the location where NCCL topology is written to. In case plugin writes a NCCL topology file and `OFI_NCCL_TOPO_FILE_TEMPLATE` is set, plugin creates a unique file using the provided template and writes topology to that file. The last six characters of the template must be `XXXXXX` and will be replaced to make the filename unique. Note that the unique topology file will not be deleted at process termination in this case.	String	Default: Unset
OFI_NCCL_ROUND_ROBIN_THRESHOLD	Adjust the maximum size of `RDMA` protocol messages that are assigned to multi-rail channels in round-robin mode. Messages larger than the threshold are multiplexed over all channels to increase network throughput. In general, users should not have to adjust this value. A very small threshold may cause the `RDMA` protocol initialization fail since RDMA protocol control messages shall not be multiplexed.	Integer	Default: 8192
OFI_NCCL_NET_LATENCY	Internode network latency in us reported to NCCL.	Integer	Any non-negative integer. Defaults to 0, unless the configured platform sets a specific value.
OFI_NCCL_EAGER_MAX_SIZE	Eager message size limit when using RDMA protocol. Message sizes greater than this limit will always be sent using RDMA write instead of eagerly.	Integer	Any non-negative integer, though must be <= ROUND_ROBIN_THRESHOLD. Defaults to 8KiB.

Running Unit Tests

Running unit tests requires a working MPI installation and a MPI setup between the communicating hosts. To install MPI, you can use standard packages provided for your linux distribution. Once MPI is setup, you can use commands like below for running any test of your choice.

mpirun -n 2 --host <host-1>,<host-2> $INSTALL_PREFIX/bin/nccl_message_transfer

Note: All tests require 2 MPI ranks to run except ring.c which requires atleast 3 ranks.

Running nccl-perf tests

To run standard nccl-perf tests with the aws-ofi-nccl plugin, you can follow the instructions below.

1. Clone the repository

git clone https://github.com/NVIDIA/nccl-tests.git

2. Build the tests

cd  nccl-tests/
make NCCL_HOME=~/nccl/build/

3. Run perf tests

NCCL_DEBUG=INFO build/all_reduce_perf -b 8 -f 2 -e 32M -c 1

If you installed the AWS libfabric plugin in a custom prefix, ensure LD_LIBRARY_PATH is set to include that prefix so the perf test binaries can find the plugin.

Getting Help

If you have any issues in building or using the package or if you think you may have found a bug, please open an issue.

Contributing

Reporting issues and sending pull requests are always welcome. To learn how you can contribute, please look at our contributing guidelines.

License

This library is licensed under the Apache 2.0 License.