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Record W2119539611 · doi:10.1145/1108768.1108809

SableSpMT

2005· article· en· W2119539611 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

VenueACM SIGSOFT Software Engineering Notes · 2005
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsMultithreadingComputer scienceJavaSpeculative multithreadingMultiprocessingOperating systemSpeculative executionstrictfpSet (abstract data type)Context (archaeology)Instruction setSoftwareReal time JavaEmbedded systemProgramming languageThread (computing)

Abstract

fetched live from OpenAlex

Speculative multithreading (SpMT) is a promising optimisation technique for achieving faster execution of sequential programs on multiprocessor hardware. Analysis of and data acquisition from such systems is however difficult and complex, and is typically limited to a specific hardware design and simulation environment. We have implemented a flexible, software-based speculative multithreading architecture within the context of a full-featured Java virtual machine. We consider the entire Java language and provide a complete set of support features for speculative execution, including return value prediction. Using our system we are able to generate extensive dynamic analysis information, analyse the effects of runtime feedback, and determine the impact of incorporating static, offline information. Our approach allows for accurate analysis of Java SpMT on existing, commodity multiprocessor hardware, and provides a vehicle for further experimentation with speculative approaches and optimisations.

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)0.000
metaresearch head score (Gemma)0.026
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.443
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.013
GPT teacher head0.232
Teacher spread0.219 · 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