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Record W2020433498 · doi:10.1145/1391732.1391735

Particle graphics on reconfigurable hardware

2008· article· en· W2020433498 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 Transactions on Reconfigurable Technology and Systems · 2008
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceGraphicsSpeedupFrame rateGraphics hardwareParticle systemGraphics pipelineParticle (ecology)Computer graphics (images)Computer graphicsGeneral-purpose computing on graphics processing unitsSoftwareField-programmable gate arrayComputational science3D computer graphicsComputer hardwareParallel computingOperating systemArtificial intelligence

Abstract

fetched live from OpenAlex

Particle graphics simulations are well suited for modeling complex phenomena such as water, cloth, explosions, fire, smoke, and clouds. They are normally realized in software as part of an interactive graphics application. The computational complexity of particle graphics simulations restricts the number of particles that can be updated in software at interactive frame rates. This article presents the design and implementation of a hardware particle graphics engine for accelerating real-time particle graphics simulations. We explore the design process, implementation issues, and limitations of using field-programmable gate arrays (FPGAs) for the acceleration of particle graphics. The FPGA particle engine processes million-particle systems at a rate from 47 to 112 million particles per second, which represents one to two orders of magnitude speedup over a 2.8 GHz CPU. Using three FPGAs, a maximum sustained performance of 112 million particles per second was achieved.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.959
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
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 head0.036
GPT teacher head0.264
Teacher spread0.228 · 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