HEP specific benchmarks of virtual machines on multi-core CPU architectures
Bibliographic record
Abstract
Virtualization technologies such as Xen can be used in order to satisfy the disparate and often incompatible system requirements of different user groups in shared-use computing facilities. This capability is particularly important for HEP applications, which often have restrictive requirements. The use of virtualization adds flexibility, however, it is essential that the virtualization technology place little overhead on the HEP application. We present an evaluation of the practicality of running HEP applications in multiple Virtual Machines (VMs) on a single multi-core Linux system. We use the benchmark suite used by the HEPiX CPU Benchmarking Working Group to give a quantitative evaluation relevant to the HEP community. Benchmarks are packaged inside VMs and then the VMs are booted onto a single multi-core system. Benchmarks are then simultaneously executed on each VM to simulate highly loaded VMs running HEP applications. These techniques are applied to a variety of multi-core CPU architectures and VM configurations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".