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Record W2118893560 · doi:10.1109/cgo.2007.5

Compilation Techniques for Real-Time Java Programs

2007· article· en· W2118893560 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicReal-Time Systems Scheduling
Canadian institutionsIBM (Canada)
Fundersnot available
KeywordsComputer scienceJavaOperating systemCompilerReal time JavaGarbage collectionJust-in-time compilationSuiteProgramming languageJava annotationJava concurrencyBenchmark (surveying)Garbage

Abstract

fetched live from OpenAlex

In this paper, we introduce the IBMreg WebSpherereg real time product, which incorporates a virtual machine that is fully Javatrade compliant as well as compliant with the Real-Time Specification for Java (RTSJ). We describe IBM's real-time Java enhancements, particularly in the area of our Testarossa (TR) ahead-of-time (AOT) compiler, our TR just-in-time (JIT) compiler, and our Metronome (Bacon, et al., 2003) deterministic garbage collector (GC). The main focus of this paper is on the various techniques employed by the TR compilers to optimize and regulate the performance of code running in a real-time Java environment through a simple Java source code example. Through the example, we highlight the additional checks required to provide a conformant RTSJ implementation as well as the performance issues with ahead-of-time code generation and the overheads required to support Metronome. We show how these checks are implemented in a production JVM, and then report the cost of the real-time changes in practice for the example as well as the SPECjvm98 benchmark suite, SPECjbb2000, and SPECjbb2005

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.700
Threshold uncertainty score0.439

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.021
GPT teacher head0.285
Teacher spread0.264 · 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

Quick stats

Citations19
Published2007
Admission routes1
Has abstractyes

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