Efficient Multi-Path NVLink/PCIe-Aware UCX based Collective Communication for Deep Learning
Bibliographic record
Abstract
High-performance communication for very large messages on modern multi-GPU nodes has become increasingly important for Deep Learning workloads. These computing nodes are equipped with state-of-the-art interconnects, such as Nvidia's NVLink and PCIe, to facilitate communications between GPUs, and GPUs with the host processors. In this paper, we take on the challenge to design efficient intra-socket GPU-to-GPU communication using multiple NVLink channels at the UCX and MPI levels, and then utilise it to design an intra-node hierarchical NVLink/PCIe-aware GPU based MPI_Allreduce to enhance Horovod + TensorFlow with different models. UCX only utilises a small portion of the available NVLink bandwidth for intra-socket GPU-to-GPU communication. We propose a novel data transfer mechanism that stripes the message across multiple intra-socket communication channels and multiple memory regions using multiple GPU streams to utilise all available NVLink paths. Our approach achieves 1.69x and 1.84x higher bandwidth for UCX and Open MPI + UCX, respectively. We observe similar bandwidth improvements for large messages for MPI point-to-point communication when compared to other MPI implementations as they are also limited by data transfers by a single path. We then propose a 3-stage hierarchical, pipelined MPI_Allreduce design that incorporates the new multi-path NVLink data transfer mechanism for intra-socket communications in the first and third stages of the collective, and PCIe and X-bus channels for inter-socket GPU communication in the second stage with minimal interference. For large messages, our proposed algorithm achieves a high speedup when compared to Spectrum MPI, Open MPI + UCX, Open MPI + HPC-X, MVAPICH2-GDR, and NCCL. We also observe significant speedup for the proposed MPI_Allreduce for Horovod with TensorFlow with a variety of Deep Learning models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".