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Record W2075789590 · doi:10.1145/1940475.1940490

Cache friendly sparse matrix-vector multiplication

2011· article· en· W2075789590 on OpenAlex
Sardar Anisul Haque, Shahadat Hossain, Marc Moreno Maza

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 communications in computer algebra · 2011
Typearticle
Languageen
FieldComputer Science
TopicMatrix Theory and Algorithms
Canadian institutionsUniversity of LethbridgeWestern University
Fundersnot available
KeywordsMultiplication (music)Computer scienceMatrix multiplicationKernel (algebra)Sparse matrixParallel computingColumn (typography)CacheMatrix (chemical analysis)Conjugate gradient methodCache-oblivious algorithmCPU cacheAlgorithmComputational complexity theoryCache algorithmsMathematicsDiscrete mathematicsCombinatorics

Abstract

fetched live from OpenAlex

Sparse matrix-vector multiplication or SpMxV is an important kernel in scientific computing. For example, in the conjugate gradient method, where SpMxV is the main computational step. Though the total number of arithmetic operations in SpMxV is fixed, reducing the probability of cache misses per operation is still a challenging area of research. In this work, we present a new column ordering algorithm for sparse matrices. We analyze the cache complexity of SpMxV when A is ordered by our technique. The numerical experiments, with very large test matrices, clearly demonstrate the performance gains rendered by our proposed technique.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.665
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0090.004
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 head0.055
GPT teacher head0.297
Teacher spread0.242 · 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