Towards building a highly-available cluster based model for high performance computing
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
In recent years, we have witnessed a growing interest in high performance computing (HPC) using a cluster of workstations. However, many challenges remain to be resolved before these systems become dependable. One of the challenges in a clustered environment is to keep system failure to the minimum level and while achieving the highest possible level of system availability. High-availability (HA) computing attempts to avoid the problems of unexpected failures through active redundancy and preemptive measures. In this paper, we propose to build HA-clusters based model for high performance computing. Our model is based on combination of both HPC and HA concepts, we also propose to investigate further the hardware and the management layers of the HA-HPC cluster design, and the parallel-applications layer (i.e. FT-MPI implementations). In this work, we focus upon the latter layer. We discuss our model, and present our simulation experiments we have carried out to evaluate our proposed model.
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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 | 0.000 |
| 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