Accelerating Deep Learning Using Interconnect-Aware UCX Communication for MPI Collectives
Bibliographic record
Abstract
Deep learning workloads on modern multi-graphics processing unit (GPU) nodes are highly dependent on intranode interconnects, such as NVLink and PCIe, for high-performance communication. In this article, we take on the challenge to design an interconnect-aware multipath GPU-to-GPU communication using unified communication X (UCX) to utilize all available bandwidth for both NVLink-based systems and those that use a mixture of NVLink and PCIe. Our proposed multipath data transfer mechanism pipelines and stripes the message across multiple intrasocket communication channels and memory regions to achieve 1.84× higher bandwidth for Open message passing interface (MPI) on NVLink-based systems and 1.23× on NVLink and PCIe systems. We then utilize this mechanism to propose a three-stage hierarchical, pipelined MPI_Allreduce design as well as a flat pipelined two-stage algorithm for two different node topologies. For large messages, our proposed algorithms achieve a high speedup when compared to other MPI implementations. We also observe significant speedup for the proposed MPI_Allreduce with Horovod + 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) | 0.000 | 0.001 |
| 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) | 0.004 | 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".