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Record W128049675 · doi:10.5753/sbac-pad.2000.41216

Efficient ECC-Based Directory Implementations for Scalable Multiprocessors

2000· article· en· W128049675 on OpenAlexaff
Kourosh Gharachorloo, Luiz André Barroso, Andreas Nowatzyk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsPQ Corporation (Canada)
Fundersnot available
KeywordsComputer scienceDirectoryMemory bandwidthEmbedded systemOperating systemLatency (audio)ScalabilityDatapathMemory controllerSemiconductor memory

Abstract

fetched live from OpenAlex

With increasing chip densities, next-generation micro-processor design have the opportunity to integrate many of the traditional system-level moules onto the same chip as the processor. This integration changes some of the design trade-offs for how and where to store directory information. One extremely attractive option is to support directory data with virtually no memory space overhead by computing memory ECC at a coarser granularity and utilizing the usused bits for storing the directory information. Compared to providing a dedicated memory and datapath for directory atorage, this approach leads to lower cost and a simpler design by requiring fewer components and pins. Furthermore, this approach leverages the low latency, high bandwidth path to memory provided by the integration of memory controllers onto the processor chip. However, without careful design, maintaining data and directory bits together can lead to potential inefficiencies in the form of extra memory bandwidth usage and memory controller occupancy, and extra memory latency. This paper describes the techniques used in the context of the Piranha design [3] to provide an efficient ECC-based directory implementation which addresses the occupancy/bandwidth and latency issues. Our approach for dealing with the occupancy/bandwidth issues involves either eliminating the extra read and write operations or performing partial memory accesses (instead of accessing the whole block). Thi is achieved by a combination of techniques which include (i) augmenting the L2 caching state to keep track of some critical directory state, (ii) making up dummy data for protocol transactions with a stale momory copy, and (iii) maintaining a partial ECC that is used to compute the combined ECC of the data and the modified directory bits without needing the actual data bits. To address the latency issues, we replicate critical directory state in different segments of the momory line which allows us to efficiently support the critical-word-first optimization by pipelining data from memory to the requester before all the data is read from memory. The combination of the above techniques also eliminates all the inefficiencies that arise due to maintaining a combined ECC for directory and data bits. Therefore, we benefit from the more efficient use of bits provided by the combined ECC with virtually no performance penalty compared to maintaining separe ECC bits for data and directory. Finally, the optimizations used in Piranha are general and applicable to other designs that use ECC-based directories.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.021
GPT teacher head0.302
Teacher spread0.281 · 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 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

Citations14
Published2000
Admission routes1
Has abstractyes

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