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Record W2075413878 · doi:10.1109/fpt.2010.5681447

A configurable framework for investigating workload execution

2010· article· en· W2075413878 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 institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaXilinxCMC Microsystems
KeywordsComputer scienceSoftwareField-programmable gate arrayEmbedded systemWorkloadInterface (matter)Block (permutation group theory)Computer architectureOperating systemComputer hardware

Abstract

fetched live from OpenAlex

Processor systems contain a limited number of hardware counters that provide some visibility for certain types of interactions, but do not support sophisticated analysis due to limited resources. By contrast, system software simulators provide multidimensional runtime data, but slowdown application execution, often resulting in an inaccurate picture of hardware/ software interactions. The ideal solution to this problem is to create a dedicated hardware unit to “watch” the processor for these types of behaviours. In this paper, we present a hardware framework that leverages an FPGA's reconfigurable fabric to investigate of workload execution behaviours on processors using a hardware-Based Analyzer for the Characterization of User Software (ABACUS). ABACUS is currently able to interface with the LEON3 processor using 1367 FFs, 1504 LUTs and 1 Block RAM on a Virtex 2Pro running at 144 MHz.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.587
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.023
GPT teacher head0.290
Teacher spread0.267 · 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