feat: support CCL Broadcast

[GordonYang1]

Summary

This PR adds Broadcast support to the shared CCL backend abstraction, including native bindings through the existing NCCL and MCCL provider layers for the public infinicclBroadcast() API and its legacy in-place infinicclBcast() alias. It also teaches Broadcast dispatch to follow the backend/device combination stored in the communicator for mixed-backend bootstrap flows, adds generated-bridge dependency tracking for header-only operation implementations, and includes CCL-only plus OpenMPI-assisted Broadcast example programs covering out-of-place, in-place, and legacy broadcast flows.

Changes

• Public API and Dispatch

  • Enable the existing infinicclBroadcast() and infinicclBcast() APIs for configured CCL backends through generated bridge dispatch.
  • Redispatch Broadcast according to the initialized backend and device stored in the supplied communicator.
  • Keep bootstrap broadcasts on the communicator's initialized MPI backend in mixed MPI and CCL configurations.

• Common CCL Implementation

  • Add a shared CCL Broadcast implementation following the existing provider-oriented AllReduce structure.
  • Validate the CCL communicator instance and map InfiniCCL data types through the configured provider before dispatch.
  • Return NotSupported when the selected provider cannot represent the requested data type.

• Existing CCL Provider Bindings

  • Extend the existing NCCL and MCCL API wrappers with thin bindings to ncclBroadcast() and mcclBroadcast().
  • Register Broadcast and the legacy in-place Bcast alias with the existing NCCL and MCCL provider layers.

• Bridge Generation

  • Track base, backend, and device headers as generated bridge dependencies.
  • Regenerate the bridge when a header-only operation implementation is added, removed, or changed.

• Examples and Validation

  • Add a thread-per-GPU single-node CCL Broadcast example.
  • Add an OpenMPI-assisted CCL Broadcast example that uses OpenMPI for rank discovery and unique-ID broadcast, then initializes a native CCL communicator for GPU data broadcast.
  • Validate out-of-place Broadcast, in-place Broadcast, and legacy in-place Bcast with the last GPU rank as root.
  • Propagate validation failures through the process exit status and reset buffers independently for every scenario.
  • Parse numeric options without exceptions and validate host-name discovery in the CCL-only example.

Platform and Backend Affected

Platform

• CPU
• NVIDIA GPU
• Iluvatar GPU
• MetaX GPU
• Moore Threads GPU
• Cambricon MLU

Backend

• OpenMPI
• MPICH
• NCCL
• MCCL

Performance Impact

• No performance impact
• Performance improved
• Performance regression possible

This adds a GPU-native CCL path for Broadcast, avoiding the existing MPI host-staging path when a supported CCL backend is selected. Existing MPI collectives are intended to remain unchanged.

Known Issues & Future Work

• CCL collective support now covers AllReduce, Broadcast, and the legacy Bcast alias; other CCL collective operations remain future work.
• Broadcast inherits the backend/device combinations and data type support of the existing CCL providers; this PR does not add a new provider or device integration.
• The shared example Metrics helper currently applies the AllReduce bus-bandwidth factor to every collective, so the Bus BW printed by the CCL Broadcast example is not used as a performance result. Correctness validation and elapsed time are unaffected.

Test Results

Test Involved Platform

• CPU
• NVIDIA GPU
• Iluvatar GPU
• MetaX GPU
• Moore Threads GPU
• Cambricon MLU

Test Involved Backend

• OpenMPI
• MPICH
• NCCL
• MCCL

Pure CCL (NCCL) on single-node NVIDIA:
ccl_all_reduce.log
ccl_broadcast.log
CCL + MPI on single-node NVIDIA:
ccl_mpi_hybrid_all_reduce.log
ccl_mpi_hybrid_broadcast.log

MPI on Heterogeneous Cluster:
mpi_all_gather.log
mpi_all_reduce.log
mpi_all_to_all.log
mpi_broadcast.log
mpi_gather.log
mpi_reduce.log
mpi_reduce_scatter.log
mpi_scatter.log
mpi_send_recv.log

Pure CCL (MCCL) on single-node MetaX:
ccl_all_reduce.log
ccl_broadcast.log

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Checklist

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Scope and Design

• Changes are minimal — no unrelated modifications were introduced (CONTRIBUTING.md §Code/General).
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General Code Hygiene

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Python Specific (if Python files changed)

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Testing

• All applicable example programs have been built and tested successfully on at least one supported heterogeneous cluster setup.

Build, CI, and Tooling

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Documentation

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Security and Safety

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