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Record W2031663903 · doi:10.1145/1345206.1345222

A case study in SIMD text processing with parallel bit streams

2008· article· en· W2031663903 on OpenAlexaff
Robert D. Cameron

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAlgorithms and Data Compression
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsComputer scienceSIMDByteParallel computingTranscodingStream processingSearch engine indexingDecoding methodsBitstreamAlgorithmComputer hardwareArtificial intelligence

Abstract

fetched live from OpenAlex

High performance SIMD text processing using the method of parallel bit streams is introduced with a case study of UTF-8 to UTF-16 transcoding. A forward transform converts byte-oriented character stream data into eight parallel bit streams. Decoding, validation and computation of UTF-8 indexed UTF-16 bit streams are performed using bit-parallel logic and shifting operations. Conversion from UTF-8 indexing to UTF-16 indexing is performed using parallel bit deletion. The inverse transform is applied to yield high and low UTF-16 byte streams which are then merged. Combined with optimization techniques for blocks of ASCII data, speed-ups of 3 to 25 times are achieved on commodity processors compared with optimized byte-at-a-time code. Further applications of the method of parallel bit streams to bulk text processing applications are briefly discussed along with future prospects for the combination of intraregister and intrachip parallelism on multicore processors.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.035
GPT teacher head0.265
Teacher spread0.230 · 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

Citations27
Published2008
Admission routes1
Has abstractyes

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