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Record W2161415359 · doi:10.1109/fccm.2005.36

FPGA Particle Graphics Hardware

2005· article· en· W2161415359 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsStratixComputer scienceField-programmable gate arrayGraphicsRendering (computer graphics)Computer hardwareSoftwareParticle systemEmbedded systemComputer graphics (images)Operating system

Abstract

fetched live from OpenAlex

Particle graphics simulations are well suited for modeling phenomena such as water, cloth, explosions, fire, smoke, and clouds. They are normal realized in software, as pan of an interactive graphics application, such as a video game. Their use in such applications is limited by the computational burden and resource competition they create for a host application. We present the design of a hardware particle machine, for implementation in an FPGA, intended for accelerating real-time panicle graphics in applications such as video games. The particle machine is a system that completely contains, manages, and executes particle graphics simulations and rendering. The particle machine is a system comprised of particle memory, a controller, and the panicle pipe, a pipelined particle update processor. The panicle pipe has been synthesized to 130 MHz, on an Altera Stratix FPGA, resulting in a potential throughput of 2.1 million PPF (panicles per frame). This throughput is achieved with minimal load on application and main system performance.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.936
Threshold uncertainty score0.284

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.026
GPT teacher head0.293
Teacher spread0.267 · 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