Bibliographic record
Abstract
In recent years high performance computing (HPC) sites have been established in various countries. In Canada connectivity between HPC sites is provided by CA*net 2, Canada’s high performance network (HPN). Users of HPC facilities currently submit jobs as, typically, program source files requiring local compilation for execution within the site and all computational resources are restricted to single site usage. Increasingly, it is the case that resource requirements extend beyond the capabilities of single HPC sites. Requirements may include more CPU cycles, access to files, user interaction and executing programs in distributed collaboration scenarios and with both present Internet and HPN based communication occurring among HPC sites. Local site policies often inhibit or disallow direct exchange of data and programs for security reasons. To solve such problems it is necessary to establish appropriate policies and protocols to enable communication between distributed processes. It is imperative to reach consensus on the issues and practices of effective, secure sharing policies among HPC sites in order to develop appropriate policies for networked resource sharing. In this paper we provide several examples to illustrate the types of computational and networking requirements which might typify inter-networking for shared distributed computing. Additionally, we summarize various points and opinions expressed by audience discussants as part of this panel presentation.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".