In search for contention-descriptive metrics in HPC cluster environment
Bibliographic record
Abstract
In this paper, we argue that the modern HPC cluster environments contain several bottlenecks both within cluster multicore nodes and between them in the cluster interconnects. These bottlenecks represent resources that can be of high demand to several jobs, concurrently executing on the cluster. As such, the jobs can compete for accessing these resources and experience performance degradation due to contention. We point out, that, although the contention for shared resources like memory hierarchy of the cluster nodes, accessing the cluster interconnects or sharing the floating point unit can incur severe performance degradation to the cluster workload, the state-of-the-art cluster schedulers do not contain adequate means of addressing it. To fill this gap, we propose a new set of metrics that models shared resource contention and represents a fine-grained information about each job's resource utilization and communication patterns. The necessary information can be obtained with the performance counters within cluster nodes and cluster interconnect monitoring between them.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".