An Efficient MPI Message Queue Mechanism for Large-scale Jobs
Bibliographic record
Abstract
The Message Passing Interface (MPI) message queues have been shown to grow proportionately to the job size for many applications. With such a behaviour and knowing that message queues are used very frequently, ensuring fast queue operations at large scales is of paramount importance in the current and the upcoming exascale computing eras. Scalability, however, is two-fold. With the growing processor core density per node, and the expected smaller memory density per core at larger scales, a queue mechanism that is blind on memory requirements poses another scalability issue even if it solves the speed of operation problem. In this work we propose a multidimensional queue traversal mechanism whose operation time and memory overhead grow sub-linearly with the job size. We compare our proposal with a linked list-based approach which is not scalable in terms of speed of operation, and with an array-based method which is not scalable in terms of memory consumption. Our proposed multidimensional approach yields queue operation time speedups that translate to up to 4-fold execution time improvement over the linked list design for the applications studied in this work. It also shows a consistent lower memory footprint compared to the array-based design.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".