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11.1 AMD InstinctTM MI300 Series Modular Chiplet Package – HPC and AI Accelerator for Exa-Class Systems

2024· article· en· W4392746141 on OpenAlexaff
A.J. Smith, Eric Chapman, Chintan Patel, Raja Swaminathan, John Wuu, Tyrone Huang, Wonjun Jung, Alexander Kaganov, H. McIntyre, R. Mangaser

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsAdvanced Micro Devices (Canada)
Fundersnot available
KeywordsModular designClass (philosophy)Computer scienceSeries (stratigraphy)Computer architectureOperating systemArtificial intelligence

Abstract

fetched live from OpenAlex

The AMD Instinct™ MI 300 Series accelerators were conceptualized to extract maximum HPC and AI capability from the latest silicon and advanced packaging technology, designed to operate as CPU hosted PCle® device, MI300X, as well as a self-hosted accelerated processing unit (APU), MI300A. AMD chiplet capabilities and advanced packaging allow AMD’s first-ever integration of data center class CPU, GPU accelerated compute, AMD Infinity Cache, and 8-stack HBM3 memory system into a single package. Observing that many Al and HPC operators are memory bound, AMD targeted MI300 to deliver over 5TBps of HBM3 peak bandwidth.

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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.035
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0350.012

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 head0.261
Teacher spread0.243 · 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
GenreOther

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

Citations51
Published2024
Admission routes1
Has abstractyes

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