MétaCan
Menu
Back to cohort
Record W2771835118 · doi:10.1109/pacrim.2017.8121911

Improving the performance of JVM startup using the shared class cache

2017· article· en· W2771835118 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsIBM (Canada)University of New Brunswick
FundersAtlantic Canada Opportunities AgencyNew Brunswick Innovation Foundation
KeywordsComputer scienceOperating systemJavaBottleneckJust-in-time compilationCacheVirtual machineBenchmark (surveying)Class (philosophy)Parallel computingIBMProgramming languageEmbedded system

Abstract

fetched live from OpenAlex

The Java Virtual Machine (JVM) is responsible for executing all Java programs, so performance improvements in the JVM, especially pertaining to the startup of an application, have wide-ranging impact. This paper presents a solution that reduces the bottleneck by analyzing and optimizing various data structures inside the JVM before a Java application starts executing. The shared class cache, which was added to IBM's J9 VM to share classes and other immutable data across multiple runs of a JVM, is expanded to include additional data. Runtime improvements of between 15% and 20% were achieved in standard benchmark programs.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.783
Threshold uncertainty score0.660

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.279
Teacher spread0.239 · 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