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Record W2091773852 · doi:10.1109/dsd.2012.13

A Simple On-Chip Optical Interconnection for Improving Performance of Coherency Traffic in CMPs

2012· article· en· W2091773852 on OpenAlexfundno aff
Sandro Bartolini, Paolo Grani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsnot available
FundersMinistère de l'Économie, de la Science et de l'Innovation - Québec
KeywordsComputer scienceInterconnectionLatency (audio)Network on a chipComputer networkMulticastBandwidth (computing)Computer architectureMultiplexingDistributed computingTelecommunications

Abstract

fetched live from OpenAlex

Nanophotonic interconnection is a promising solution for inter-core communication in future chip multiprocessors (CMPs). Main benefits derive from its intrinsic low-latency and high-bandwidth, especially when employing wavelength division multiplexing (WDM), as well as reduced power requirements when compared to electronic NoCs. Existing works on optical NoCs (ONoC) mainly concentrate on relatively complex proposals needed to host the whole CMP traffic. In some proposals complexity is increased also from the need of an electronic network for preliminary pathsetup in the optical one. This paper proposes to enhance a conventional NoC with only a simple photonic structure, a ring, and aims at investigating its suitability to support the low-latency transmission of small latency-critical coherency control messages as to improve performance of multithreaded applications. In particular, our proposed scheme supports fast multicast transmission of invalidation messages. We have simulated Parsec benchmarks on an 8 core full-system CMP. Results show that a careful selection of coherency control messages to be forwarded to the photonic ring allows improving execution time up to 19%, with an average of 6% across all considered benchmarks. We discuss how different selections of messages, i.e. related to read and/or write operations, affect results and single out the most profitable set. Moreover, we show that the sharing behavior of benchmarks has a central role in the final performance.

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 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.788
Threshold uncertainty score0.242

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 head0.014
GPT teacher head0.231
Teacher spread0.217 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations16
Published2012
Admission routes1
Has abstractyes

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