An Efficient MPI Message Queue Mechanism for Large-scale Jobs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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