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Record W3017116334 · doi:10.1145/3392032

SIMT-X

2020· article· en· W3017116334 on OpenAlexafffund
Anita Tino, Caroline Collange, André Seznec

Bibliographic record

VenueACM Transactions on Architecture and Code Optimization · 2020
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsSimon Fraser University
FundersMitacs
KeywordsComputer scienceThread (computing)Parallel computingMicroarchitectureSIMDInstruction setOut-of-order executionComputer architectureLatency (audio)Operating system

Abstract

fetched live from OpenAlex

This work introduces Single Instruction Multi-Thread Express (SIMT-X), a general-purpose Central Processing Unit (CPU) microarchitecture that enables Graphics Processing Units (GPUs)-style SIMT execution across multiple threads of the same program for high throughput, while retaining the latency benefits of out-of-order execution, and the programming convenience of homogeneous multi-thread processors. SIMT-X leverages the existing Single Instruction Multiple Data (SIMD) back-end to provide CPU/GPU-like processing on a single core with minimal overhead. We demonstrate that although SIMT-X invokes a restricted form of Out-of-Order (OoO), the microarchitecture successfully captures a majority of the benefits of aggressive OoO execution using at most two concurrent register mappings per architectural register, while addressing issues of partial dependencies and supporting a general-purpose Instruction Set Architecture (ISA).

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.045
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0450.020

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 head0.018
GPT teacher head0.238
Teacher spread0.220 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2020
Admission routes2
Has abstractyes

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