hi， I want to know the performance benchmark about gloo with nccl
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Description
I want to know how Gloo compares to NCCL in terms of performance for distributed training on NVIDIA GPUs (CUDA), as we currently plan to use Gloo for distributed inference in Java with javacpp-pytorch.
Key Points:
Gloo vs. NCCL:
NCCL is optimized for NVIDIA GPUs (CUDA), offering higher throughput and lower latency in GPU clusters.
Gloo is a cross-platform alternative but may underperform NCCL in CUDA environments unless specific optimizations are applied.
Java Integration with PyTorch (javacpp-pytorch):
Gloo is supported in PyTorch, but NCCL is the default for GPU training.
If using Gloo in Java, ensure compatibility with CUDA and distributed inference workflows.
Would you like a deeper comparison (e.g., benchmarks, setup guidance) for your specific use case?
thanks
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Research direction
No repository files, tests, or entry points are mentioned. First clarify whether the request is for benchmark data or Java/CUDA setup guidance, then define the hardware, workloads, and metrics; done means a reproducible Gloo-versus-NCCL comparison or documented guidance.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- cpp, java, pytorch
- Domain
- distributed-systems, machine-learning, performance
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 18/100