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.
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.
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,FI_CONTEXT2) - 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.
aws-ofi-nccl requires a working installation of libfabric. You can
find the instructions for installing libfabric at
libfabric installation.
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
To enable UBSAN (Undefined Behaviour Sanitizer), use the following config option:
--enable-ubsan Enable undefined behaviour checks with UBSAN
To enable memory access checks with ASAN (disabled by default), use the following config option:
--enable-asan Enable ASAN memory access checks
In case plugin is configured with --enable-asan and the executable
binary is not compiled and linked with ASAN support, it is required to
preload the ASAN library, i.e., run the application with export LD_PRELOAD=<path to libasan.so>.
In case plugin is configured with --enable-asan and the plugin is
run within a CUDA application, environment variable ASAN_OPTIONS
needs to include protect_shadow_gap=0. Otherwise, ASAN will crash on
out-of-memory.
NCCL currently has some memory leaks and ASAN reports memory leaks by
default on process exit. To avoid warnings on such memory leaks, e.g.,
only invalid memory accesses are of interest, add detect_leaks=0 to
ASAN_OPTIONS.
To enable memory access checks with valgrind (disabled by default), use the following config option:
-with-valgrind[=PATH] Enable valgrind memory access checks
Use optional parameter PATH to point the build to a custom path
where valgrind is installed. The memory access checkers ASAN and
valgrind are mutually exclusive.
In case plugin allocates a block of memory to store multiple structures, redzones are added between adjacent objects such that memory access checker can detect access out of the boundaries of these objects. The redzones are dedicated memory areas that are marked as not accessible by memory access checkers. The default size of redzones is 16 bytes in case memory access checks are enabled and 0 otherwise. To control the size of redzones, use the following config option:
MEMCHECK_REDZONE_SIZE=REDZONE_SIZE Size of redzones in bytes
Redzones are required to be a multiple of 8 due to ASAN shadow-map granularity.
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.
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 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.
To run standard nccl-perf tests with the aws-ofi-nccl plugin, you can
follow the instructions below.
- Clone the repository
git clone https://github.com/NVIDIA/nccl-tests.git
- Build the tests
cd nccl-tests/
make MPI=1 MPI_HOME=/path/to/mpi CUDA_HOME=/path/to/cuda NCCL_HOME=/path/to/nccl
- Run perf tests
NCCL_DEBUG=INFO mpirun -np 2 build/all_reduce_perf -b 8 -f 2 -e 32M -c 1 -g 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.
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.
Reporting issues and sending pull requests are always welcome. To learn how you can contribute, please look at our contributing guidelines.
This library is licensed under the Apache 2.0 License.