MétaCan
Menu
Back to cohort
Record W4247063686 · doi:10.1145/2499369.2465563

FTL <sup>2</sup>

2013· article· en· W4247063686 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

VenueACM SIGPLAN Notices · 2013
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Storage Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceFlash file systemCacheGarbage collectionOperating systemOverhead (engineering)Flash memoryNAND gateParallel computingFlash (photography)Flash memory emulatorEmbedded systemGarbageComputer memoryProgramming languageAlgorithmLogic gate

Abstract

fetched live from OpenAlex

NAND flash memory has been widely used to build embedded devices such as smartphones and solid state drives (SSD) because of its high performance, low power consumption, great shock resistance and small form factor. However, its lifetime and performance are greatly constrained by partial page updates, which will lead to early depletion of free pages and frequent garbage collections. On the one hand, partial page updates are prevalent as a large portion of I/O does not modify file contents drastically. On the other hand, general-purpose cache usually does not specifically consider and eliminate duplicated contents, despite its popularity. In this paper, we propose a hybrid approach called FTL 2 , which employs both logging and mapping techniques in flash translation layer (FTL), to tackle the endurance problem and performance degradation caused by partial page updates in flash memory. FTL 2 logs the latest contents in a high-speed temporary storage, called Content Cache to handle partial page updates. Experimental results show that FTL 2 can greatly reduce page writes and postpone garbage collections with a small overhead.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0040.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.003

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 head0.020
GPT teacher head0.248
Teacher spread0.227 · 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