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Record W4401450480 · doi:10.1145/3675378

HIPRT: A Ray Tracing Framework in HIP

2024· article· en· W4401450480 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

VenueProceedings of the ACM on Computer Graphics and Interactive Techniques · 2024
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsAdvanced Micro Devices (Canada)
Fundersnot available
KeywordsRay tracing (physics)Computer scienceRendering (computer graphics)Computer graphics (images)Distributed ray tracingTree traversalSoftware renderingScalabilityPath tracingBeam tracingTracingGraphics3D computer graphicsOperating systemProgramming language

Abstract

fetched live from OpenAlex

We present HIPRT, an open-source ray tracing framework in HIP. HIPRT provides a versatile, cross-platform solution for professional rendering on contemporary many-core architectures. The core of the framework relies on the bounding volume hierarchy (BVH) with scalable construction algorithms and efficient ray traversal, employing hardware acceleration on AMD GPUs. From a user perspective, we aim at minimalist and user-friendly API design, allowing a user to write ray tracing applications only in a few lines of code. Unlike other graphics APIs that couple ray tracing and shading together, HIPRT provides only ray tracing functionality and thus can be seamlessly integrated into existing rendering environments. To demonstrate advanced features of HIPRT, we integrated it into the three rendering systems: Blender Cycles, PBRT-v4, and Radeon ProRender.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.790
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0000.001
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 head0.018
GPT teacher head0.301
Teacher spread0.283 · 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