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Record W2184502091 · doi:10.4172/2329-6755.1000199

Accelerating the GMRES Solver with Block ILU (K) Preconditioner on GPUs in Reservoir Simulation

2015· article· en· W2184502091 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Geology & Geosciences · 2015
Typearticle
Languageen
FieldComputer Science
TopicMatrix Theory and Algorithms
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates - Technology FuturesCMG Reservoir Simulation FoundationUniversity of Calgary
KeywordsPreconditionerGeneralized minimal residual methodSolverParallel computingBlock (permutation group theory)Computer scienceComputational scienceMathematicsAlgorithmIterative methodGeometryProgramming language

Abstract

fetched live from OpenAlex

This paper studies the parallelization of the restarted GMRES solver, GMRES (m), and the block ILU (k) preconditioner on GPUs used in petroleum reservoir simulations. The difficulty is how to accelerate this preconditioner with a variable block size. In this paper, parallel solution techniques for block triangular systems are proposed, which work for matrices with an arbitrary block size. These techniques also work with an arbitrary level k for the block ILU (k) preconditioner. Numerical experiments show that the GPU-based linear solver GMRES (m) is much faster than its CPU version.

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)0.002
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.193

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.039
GPT teacher head0.285
Teacher spread0.246 · 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