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ExaNoDe: Combined Integration of Chiplets on Active Interposer with Bare Dice in a Multi-Chip-Module for Heterogeneous and Scalable High Performance Compute Nodes

2020· preprint· en· W3109527654 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

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
FundersHorizon 2020 Framework ProgrammeAgence Nationale de la Recherche
KeywordsScalabilityComputer scienceModular designInterposerDiceNode (physics)Computer architectureField-programmable gate arrayContext (archaeology)Parallel computingSupercomputerChipEfficient energy useRouting (electronic design automation)Embedded systemComputational scienceEngineeringMaterials scienceElectrical engineeringNanotechnologyOperating systemLayer (electronics)

Abstract

fetched live from OpenAlex

In the context of high performance computing (HPC), energy efficiency and computing density are key for targeting exascale architectures. Close integration of chiplets, active interposer and field programmable gate arrays (FPGA) paves the way for dense, efficient and modular compute nodes. In this paper, we detail the ExaNoDe multi-chip-module (MCM) combining the integration of a substrate, an active interposer, some chiplets and bare dice. The reported MCM demonstrates that the multi-level integration flow enables tight integration of hardware accelerators in a heterogeneous HPC compute node.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.344
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.032
GPT teacher head0.268
Teacher spread0.236 · 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