This change makes MSCCL++ automatically select CUDA architectures based
on the build environment. If an NVIDIA GPU is detected, the build
targets the native GPU architecture for optimal performance; otherwise,
it falls back to building for multiple architectures for portability.
When building for the native architecture, FP8 support is automatically
enabled for “a-series” GPUs (e.g., sm_100a), allowing the appropriate
optimized code paths to be picked up.
* Now `NvlsConnection` internally reuses `GpuIpcMem` for multicast
memory handling.
* Removed unnecessary barriers from `connectNvlsCollective()` (CUDA API
handles this automatically).
* Updated `GpuIpcMem::map()` and `GpuIpcMem::mapMulticast()` to return a
shared pointer with custom deleter for unmapping, which prevents misuse
of raw pointers and reduces states to be stored in the `GpuIpcMem`
instance.
* Now for `RuntimeIpc` type handles, for consistency with other types,
`cudaIpcOpenMemHandle` will be called in `GpuIpcMem::map()` instead of
the ctor of `GpuIpcMem`.
---------
Co-authored-by: Binyang Li <binyli@microsoft.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Copilot <198982749+Copilot@users.noreply.github.com>
Co-authored-by: Binyang2014 <9415966+Binyang2014@users.noreply.github.com>
Add `GpuIpcMemHandle` that is a generic GPU memory handle that covers
all existing methods for GPU memory mapping. This PR fixes issues that
fail to properly fallback to a feasible type of memory handle on the
importing environment. It also consolidates code for creating or
destroying various memory handles into a single RAII wrapper.
---------
Co-authored-by: Binyang Li <binyli@microsoft.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Copilot <198982749+Copilot@users.noreply.github.com>
Co-authored-by: Binyang2014 <9415966+Binyang2014@users.noreply.github.com>
* Updated Dockerfiles and the build script to support CUDA 13.0
* Added Python3 venv which is required since Python 3.12
* Updated the default MLNX-OFED version to the LTS version
* Added docker push instruction for multi-arch manifest
- Remove cuda11 support for nccl-test pipeline, since nccl build failed
for cuda11.
- Update to cuda12.9 for CI pipeline. Will consider dropping cuda11
support add cuda13 support in near future
Tune the nThreadsPerBlock for message size in 32KB to 256KB range for FP8 and Half datatype on MI300.
---------
Co-authored-by: Binyang Li <binyli@microsoft.com>
Introduce handle cache for AMD platform.
Avoid reaching handle limitation if we open too much IPC handles
For nvidia, we don't need this feature since nvidia will count the
handle reference internally and reuse the same handle if already be
opened
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Binyang2014 <9415966+Binyang2014@users.noreply.github.com>
Co-authored-by: Changho Hwang <changhohwang@microsoft.com>
* Added `port` and `gidIndex` field in the IB endpoint config (and
`deviceIndex` field for future usages)
* Added `MSCCLPP_IBV_SO` env variable to specify a custom libibverbs.so
* Added `--ib_gid_index` CLI option to `mp_unit_tests`
* Other minor fixes
Add an RAII guard that sets a proper GPU device before a CUDA API call.
We may change this stateful in the future to minimize `cudaGetDevice()`
calls. This PR fixes a bug of the tutorial 01.
Minimal fix to make things work. We need a more careful look at
preventing silent fallback of nanobind when it fails to (properly)
construct a C++ STL object with mscclpp instances.
Use mscclpp::DataType to replace the following types in API interface:
int dtype;
ncclDataType_t dtype;
Add data type conversion:
Convert ncclDataType_t to mscclpp::DataType
The key purpose is handling all mscclpp objects' memory internally by
hiding shared pointers from user APIs.
* `Connection` class is now a wrapper of `BaseConnection` class that is
equivalent to the previous `Connection` class
* `connect()` methods now return `Connection` instead of
`std::shared_ptr<Connection>`
* Removed `connectOnSetup()` method
This PR introduces three new operations to enhance flexibility and
performance at executor.
One operation can be invoked directly via the DSL API and two operations
are created through fusion of existing operations, reducing overhead and
improving efficiency.
1. Port Channel Put Packet (Direct DSL API Call): Sends data from pkt
format to the remote side in pkt format via the port channel. Both
source and destination buffers must be scratch.
2. Reduce Copy Packet (Fusion):
Reduce Packet+Copy Packet=Reduce Copy Packet
Triggered when the destination buffer of Reduce Packet matches the
source buffer of Copy Packet.
Purpose: Combine reduction and copy into a single step for better
performance.
3. Reduce Copy Send Packet (Fusion):
Reduce Copy Packet+Put Packet=Reduce Copy Send Packet (when dst buffer
of Reduce Copy Packet matches src buffer of Put Packet)
Reduce Copy Packet+Read Put Packet=Reduce Copy Send Packet (when dst pkt
buffer of Reduce Copy Packet matches src buffer of Read Put Packet)
Purpose: Combine reduction, copy, and send operations into one optimized
pipeline.
Fusion Diagram
Reduce Packet + Copy Packet → Reduce Copy Packet
Reduce Copy Packet + Put Packet → Reduce Copy Send Packet
Reduce Copy Packet + Read Put Packet → Reduce Copy Send Packet
Beyond this, this PR adjust the AllReduce 2 Node algorithm:
Message Size | Latency (µs)
1K | 15.34
2K | 15.88
4K | 15.71
8K | 16.01
16K | 15.88
32K | 16.21
64K | 16.90
128K | 18.24
256K | 20.39
512K | 25.26
1M | 32.74
2M | 53.64
