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Record W635147021

Software Performance and Scalability: A Quantitative Approach

2009· book· en· W635147021 on OpenAlexaboutno aff
Henry H. Liu

Bibliographic record

Venuenot available
Typebook
Languageen
FieldComputer Science
TopicSoftware System Performance and Reliability
Canadian institutionsnot available
Fundersnot available
KeywordsScalabilityComputer scienceSoftwareProfiling (computer programming)Software metricSoftware engineeringSoftware developmentSoftware qualityOperating system
DOInot available

Abstract

fetched live from OpenAlex

Praise from the Reviewers:"The practicality of the subject in a real-world situation distinguishes this book from othersavailable on the market."—Professor Behrouz Far, University of Calgary"This book could replace the computer organization texts now in use that every CS and CpEstudent must take. . . . It is much needed, well written, and thoughtful."—Professor Larry Bernstein, Stevens Institute of TechnologyA distinctive, educational text onsoftware performance and scalabilityThis is the first book to take a quantitative approach to the subject of software performance and scalability

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)0.007
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.011
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.023
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.005
Science and technology studies0.0010.009
Scholarly communication0.0090.018
Open science0.0020.004
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0110.003

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 head0.017
GPT teacher head0.233
Teacher spread0.216 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations41
Published2009
Admission routes1
Has abstractyes

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