MétaCan
Menu
Back to cohort

Compiler-Guaranteed Safety in Code-Copying Virtual Machines

2008· book-chapter· en· W128638405 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

VenueLecture notes in computer science · 2008
Typebook-chapter
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceCopyingCompilerProgramming languageDead code eliminationJust-in-time compilationCode generationMachine codeProgrammerCode (set theory)Source codeVirtual machineParallel computingOperating systemObject codeKey (lock)Set (abstract data type)

Abstract

fetched live from OpenAlex

Virtual Machine authors face a difficult choice between low performance, cheap interpreters, or specialized and costly compilers. A method able to bridge this wide gap is the existing code-copying technique that reuses chunks of the VM’s binary code to create a simple JIT. This technique is not reliable without a compiler guaranteeing that copied chunks are still functionally equivalent despite aggressive optimizations. We present a proof-of-concept, minimal-impact modification of a highly optimizing compiler, GCC. A VM programmer marks chunks of VM source code as copyable. The chunks of native code resulting from compilation of the marked source become addressable and self-contained. Chunks can be safely copied at VM runtime, concatenated and executed together. This allows code-copying VMs to safely achieve speedup up to 3 times, 1.67 on average, over the direct interpretation. This maintainable enhancement makes the code-copying technique reliable and thus practically usable.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.469
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0040.002
Research integrity0.0000.001
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 head0.017
GPT teacher head0.254
Teacher spread0.237 · 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