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Record W2156403661 · doi:10.1109/l-ca.2008.13

Multicore DIMM: an Energy Efficient Memory Module with Independently Controlled DRAMs

2008· article· en· W2156403661 on OpenAlexaff
Jung Ho Ahn, Jacob Leverich, Robert Schreiber, Norman P. Jouppi

Bibliographic record

VenueIEEE Computer Architecture Letters · 2008
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsHewlett-Packard (Canada)
Fundersnot available
KeywordsComputer scienceRegistered memoryEmbedded systemInterleaved memoryDramMulti-core processorVirtual memoryMemory managementSemiconductor memoryMemory bandwidthMemory controllerMemory mapComputer hardwareOperating system

Abstract

fetched live from OpenAlex

Demand for memory capacity and bandwidth keeps increasing rapidly in modern computer systems, and memory power consumption is becoming a considerable portion of the system power budget. However, the current DDR DIMM standard is not well suited to effectively serve CMP memory requests from both a power and performance perspective. We propose a new memory module called a multicore DIMM, where DRAM chips are grouped into multiple virtual memory devices, each of which has its own data path and receives separate commands. The Multicore DIMM is designed to improve the energy efficiency of memory systems with small impact on system performance. Dividing each memory modules into 4 virtual memory devices brings a simultaneous 22%, 7.6%, and 18% improvement in memory power, IPC, and system energy-delay product respectively on a set of multithreaded applications and consolidated workloads.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.205
Teacher spread0.196 · 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 designBench or experimental
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

Citations85
Published2008
Admission routes1
Has abstractyes

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