Fig. 1. Overlay Metacomputers and the Trellis Security Infrastructure
Bibliographic record
Abstract
Researchers often have non-privileged access to a variety of high-performance computer (HPC) systems in different administrative domains, possibly across a wide-area network. 1 Consequently, the security infrastructure becomes an important component of an overlay metacomputer: a user-level aggregation of HPC systems. The Trellis Security Infrastructure (TSI) is layered on top of the widely-deployed Secure Shell (SSH) and systems administrators only need to provide unprivileged accounts to the users. The contribution of TSI is in demonstrating that a single signon (SSO) system, for a variety of use-case scenarios, can be implemented without requiring a completely new security infrastructure. We describe the use of TSI for a Canada-wide overlay metacomputer, for computational workloads (i.e., CISS-3) that spanned 22 administrative domains, at its peak had over 4,000 concurrent jobs, and included a new distributed file system (i.e., Trellis NFS). Key words: security, single sign-on, metacomputing, computational science, capacity computing, global job scheduler, distributed file system 1 Please see
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.067 | 0.012 |
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".