MétaCan
Menu
Back to cohort
Record W2291811177 · doi:10.1109/socc.2015.7406990

Exploiting multi-band transmission line interconnects to improve the efficiency of cache coherence in multiprocessor system-on-chip

2015· article· en· W2291811177 on OpenAlexfundno aff
Qi Hu, Kejun Wu, Peng Liu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaMinistère de l'Économie, de la Science et de l'Innovation - Québec
KeywordsComputer scienceMESI protocolCache coherenceIndirectionCache algorithmsCacheCache invalidationMESIF protocolSmart CacheParallel computingBus sniffingCache pollutionCoherence (philosophical gambling strategy)CPU cacheOperating system

Abstract

fetched live from OpenAlex

Main-stream general-purpose microprocessors integrate a growing number of cores on-chip, requiring high-performance interconnects and efficient cache coherence for data transmission and sharing. Conventional directory-based cache coherence has high indirection overhead, which adds to the critical path of data requests and lowers overall system performance. In fact, with globally shared high-performance interconnects, cache coherence could be optimized and the indirection overhead could be relieved. This paper explores the use of multi-band transmission lines to implement globally shared interconnects. Taking advantage of the aggregate frequency band resources, the proposed interconnect supports efficient multi-cast, and helps improve the efficiency of cache coherence with augmented parallelism. Coherence indirections are avoided, and we have seen an average of 17% boost in application performance, as well as an average of 18% throughput improvement compared to an implementation of conventional cache coherence on single-band transmission line based interconnects.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.777
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.047
GPT teacher head0.291
Teacher spread0.244 · 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
GenreMethods

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

Citations1
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicParallel Computing and Optimization TechniquesFrench-language works237,207