Bibliographic record
Abstract
As we move toward the Exascale era, HPC systems are becoming more complex, introducing increasing levels of heterogeneity in communication channels. This leads to variations in communication performance at different levels of hierarchy within modern HPC systems. Consequently, communicating peers such as MPI processes should be mapped onto the target cores in a topology-aware fashion so as to avoid message transmissions over slower channels. This is especially true for collective communications due to the global nature of their communication patterns and their vast use in many of parallel applications. In this paper, we exploit the rank reordering mechanism of MPI to realize run-time topology awareness for collective communications and in particular MPI_Allgather. To this end, we propose four fine-tuned mapping heuristics for various communication patterns and algorithms commonly used in MPI_Allgather. The heuristics provide a better match between the collective communication pattern and the topology of the target system. Our experimental results with 4096 processes show that MPI rank reordering using the proposed fine-tuned mapping heuristics can provide up to 78% reduction in MPI_Allgather latency at the micro-benchmark level. At the application level, we can achieve up to 34% reduction in execution time. The results also show that the proposed heuristics significantly outperform the Scotch library which provides a general-purpose graph mapping library.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".