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Record W2981413263 · doi:10.1145/3341301.3359640

An analysis of performance evolution of Linux's core operations

2019· article· en· W2981413263 on OpenAlexaff
Xiang Ren, Kirk Rodrigues, Luyuan Chen, Juan Camilo Vega, Michael Stumm, Ding Yuan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Storage Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceLinux kernelScalabilityOperating systemOverhead (engineering)Multi-core processorSystem callWorkloadKernel (algebra)Context switchLatency (audio)Simple (philosophy)Context (archaeology)Distributed computingTelecommunications

Abstract

fetched live from OpenAlex

This paper presents an analysis of how Linux's performance has evolved over the past seven years. Unlike recent works that focus on OS performance in terms of scalability or service of a particular workload, this study goes back to basics: the latency of core kernel operations (e.g., system calls, context switching, etc.). To our surprise, the study shows that the performance of many core operations has worsened or fluctuated significantly over the years. For example, the select system call is 100% slower than it was just two years ago. An in-depth analysis shows that over the past seven years, core kernel subsystems have been forced to accommodate an increasing number of security enhancements and new features. These additions steadily add overhead to core kernel operations but also frequently introduce extreme slowdowns of more than 100%. In addition, simple misconfigurations have also severely impacted kernel performance. Overall, we find most of the slowdowns can be attributed to 11 changes.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.015
GPT teacher head0.268
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations29
Published2019
Admission routes1
Has abstractyes

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