Topology-Aware GPU Selection on Multi-GPU Nodes
Why this work is in the frame
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Bibliographic record
Abstract
GPU accelerators have successfully established themselves in modern HPC clusters due to their high performance and energy efficiency. To increase the GPU computational power in a cluster node and tackle larger problems, multi-GPU nodes have become the platform of choice for scientific applications. In a multi-GPU node, GPU devices are interconnected together via different communication channels. Thus, intranode inter-process communications among GPUs may traverse different paths with different latency and bandwidth capacity. As the number of GPUs within a multi-GPU node increases, the topology of GPU interconnects becomes more hierarchical, effectively increasing the heterogeneity of the GPU communication channels. In this paper, we provide evidence that the performance of different intranode GPU communication channels can be considerably different from each other. This is specially true for larger message sizes. Taking this into account, our goal in this work is to efficiently assign the available GPU devices on a multi-GPU node to MPI processes in order to improve the GPU-to-GPU communication performance. We tackle this challenge by proposing a topology-aware GPU selection scheme. Our scheme is capable of efficiently mapping MPI processes to the available intranode GPU devices, in a way that more intensive inter-process GPU communications take place on the more efficient communication channels. Our experimental results show that our topology-aware GPU selection scheme can improve the communication performance of the microbenchmarks with different communication patterns, specifically those with weighted and asymmetrical communications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it