Bibliographic record
Abstract
Research on Graph Convolutional Networks (GCNs) has increasingly gained popularity in recent years due to the powerful representational capacity of graphs. A common assumption in traditional synchronous parallel training of GCNs using multiple GPUs is that load is perfectly balanced. However, this assumption may not hold in a real-world scenario where there can be imbalances in workloads among GPUs for various reasons. In a synchronous parallel implementation, a straggler in the system can limit the overall speed up of parallel training. To address these performance issues, this research investigates approaches for asynchronous decentralized parallel training of GCNs on a GPU cluster. The techniques investigated are based on graph clustering and the Gossip protocol. The research specifically adapts the approach of Cluster GCN, which uses graph partitioning for SGD based training, and combines with a gossip algorithm specifically designed for a GPU cluster to periodically exchange gradients among randomly chosen partners (GPUs). In addition, it incorporates a work pool mechanism for load balancing among GPUs. The gossip algorithm is proven to be deadlock free. The implementation is performed on a deep learning cluster with 8 Tesla V100 GPUs per compute node, and PyTorch and DGL as the software platforms. Experiments are conducted on different benchmark datasets. The results demonstrate superior performance with similar accuracy scores, as compared to traditional synchronous training which uses “all reduce” to synchronously accumulate parallel training results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".