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Record W2033171136 · doi:10.1145/1923947.1924007

Cache-oblivious algorithms, a key to better performance

2010· article· en· W2033171136 on OpenAlexaff
Yang Yang, Simon Guo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsIBM (Canada)
Fundersnot available
KeywordsComputer scienceCacheCentral processing unitCPU shieldingCPU cacheParallel computingCache coloringOperating systemKey (lock)Pipeline burst cacheEmbedded system

Abstract

fetched live from OpenAlex

The cheapening of CPU cores in recent years, created a very complex set of circumstances. As the number of CPU cores per computer became greater, difficulties arose which meant that memory access became the bottle-neck for performance. CPUs of the current day and age far superseded their predecessors. 20 years ago, the frequency of the CPU was equivalent to that of the memory bus. This trend changed during the 1990s as hardware designers were able to increase the CPU frequency at a rapid rate. However, the frequency rate of the memory bus stayed the same for architectural reasons. Hardware designer have since come up with data access acceleration techniques, one of which, is the CPU cache. The workshop provided a walkthrough as to why CPU caches are important for software developers and the various cache algorithms that were designed to exploit the CPU cache without having the size of the cache as an explicit parameter.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.908
Threshold uncertainty score0.363

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 science0.0010.000
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 head0.010
GPT teacher head0.241
Teacher spread0.231 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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