Scalable RMA-based Communication Library Featuring Node-local NVMs
Bibliographic record
Abstract
Remote Memory Access (RMA) is a useful communication interface to develop high-performance applications with complicated communication patterns. However, the data scales of such applications are still limited by the totally available main memory capacity. To accommodate extreme scale executions of those applications, we developed vGASNet, which is an RMA-based communication library that exploits the capacity of non-volatile memory (NVM) on each node. With vGASNet, NVM devices on nodes compose a large shared address space. Under this model, the key for good application performance is to reduce bandwidth bottlenecks. First, since NVM is much slower than DRAM, reducing the amounts of NVM accesses is important. For this purpose, vGASNet regards DRAM of each computation node as a cache of NVM. Next, one of bottleneck sources in RMA is caused by access contention. In order to mitigate its effects, vGASNet adopts cooperative cache mechanism, which make multiple caches of an object on several nodes. Our evaluation using vGASNet shows the above cache mechanism improves the scalability of RMA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".