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Record W3044002203 · doi:10.5539/cis.v13n3p89

Main Scientific and Technological Problems in the Field of Architectural Solutions for Supercomputers

2020· article· en· W3044002203 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueComputer and Information Science · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Data Processing Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceGraphicsField (mathematics)GraphGeneral-purpose computing on graphics processing unitsData scienceComputer architectureArtificial intelligenceHuman–computer interactionMultimediaTheoretical computer scienceComputer graphics (images)

Abstract

fetched live from OpenAlex

In this paper author describes creation of a domestic accelerator processor capable of replacing NVIDIA GPGPU graphics processors for solving scientific and technical problems and other tasks requiring high performance, but which are characterized by good or medium localization of the processed data. Moreover, this paper illustrates creation of a domestic processor or processors for solving the problems of creating information systems for processing big data, as well as tasks of artificial intelligence (deep learning, graph processing and others). Therefore, these processors are characterized by intensive irregular work with memory (poor and extremely poor localization of data), while requiring high energy efficiency. The author points out the need for a systematic approach, training of young specialists on supporting innovative research.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.964
Threshold uncertainty score0.118

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 communication0.0000.001
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 head0.024
GPT teacher head0.258
Teacher spread0.234 · 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