Metis: a profiling toolkit based on the virtualization of hardware performance counters
Bibliographic record
Abstract
Abstract Background With wide application of virtualization technology, the demand is increasing for performance analysis and system diagnosis in virtualization environment. There are some profiling toolkits based on hardware events, such as OProfile in native Linux and Xenoprof in Xen virtual machine environment. However, sometimes users in different domains need monitor different hardware events individually at the same time. For programming and profiling in environment for virtual machine, it may become popular in the coming future. In this paper, we present Metis, a system-wide profiling toolkit for Xen virtual machine environment based on the virtualization of hardware performance counters. Methods Virtualization of hardware performance counters is used to enable profiling of processes and routines running in the domain or Xen virtual machine monitor. Results This toolkit allows multiple users in different domains to monitor different hardware events simultaneously in Xen virtual machine environment, obtaining the distribution of hardware events such as clock cycles, instruction execution and cache misses, etc. Our experiments with a real-world benchmark demonstrate the good performance of Metis. Conclusion Comparing to all the existing profiling toolkits, Metis is different which enables multiple users in different virtual machines to monitor different CPU events simultaneously, and users in different domains can use this toolkit individually without affecting each other. We apply a popular benchmark to verify the correctness of Metis and its cool features.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".