CHOPIN: Scalable Graphics Rendering in Multi-GPU Systems via Parallel Image Composition
Bibliographic record
Abstract
The appetite for higher and higher 3D graphics quality continues to drive GPU computing requirements. To satisfy these demands, GPU vendors are moving towards new architectures, such as MCM-GPU and multi-GPUs, that connect multiple chip modules or GPUs with high-speed links (e.g., NVLink and XGMI) to provide higher computing capability. Unfortunately, it is not clear how to adequately parallelize the rendering pipeline to take advantage of these resources while maintaining low rendering latencies. Current implementations of Split Frame Rendering (SFR) are bottlenecked by redundant computations and sequential inter-GPU synchronization, and fail to scale as the GPU count increases. In this paper, we propose CHOPIN, a novel SFR scheme for multi-GPU systems that exploits the parallelism available in image composition to eliminate the bottlenecks inherent to existing solutions. CHOPIN composes opaque sub-images out-of order, and leverages the associativity of image composition to compose adjacent sub-images of transparent objects asynchronously. To mitigate load imbalance across GPUs and avoid inter-GPU network congestion, CHOPIN includes two new scheduling mechanisms: a draw-command scheduler and an image composition scheduler. Detailed cycle-level simulations on eight real-world game traces show that, in an 8-GPU system, CHOPIN offers speedups of up to 1.56× (1.25× gmean) compared to the best prior SFR implementation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".