Assessing the Ability of Computation/Communication Overlap and Communication Progress in Modern Interconnects
Why this work is in the frame
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Bibliographic record
Abstract
Computation/communication overlap is one of the fundamental techniques in hiding communication latency. Independent progress support in messaging layer, network interface offload capability and application usage of non-blocking communications are believed to increase overlap and yield performance benefits. In this paper, we analyze four MPI implementations on top of three high-speed interconnects (InfiniBand, Myrinet and iWARP Ethernet) in their ability to support overlap and communication progress. The results confirm that the offload ability needs to be supported with communication progress to increase the level of overlap. Our progress engine micro-benchmark results show that in all examined networks transferring small messages makes an acceptable level of progress and overlap. On the other hand, in most cases, transferring large messages does not make progress independently, decreasing the chances of overlap in applications.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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