Distributed Training of Neural Radiance Fields: A Performance Characterization
Bibliographic record
Abstract
Implicit neural representation is an emerging method that leverages deep neural networks and learned parameters to represent 3D scenes efficiently and accurately. Neural radiance field (NeRF) is a state-of-art implicit representation that achieves photorealistic 3D reconstruction with compact neural network models. However, as the complexity and scale of the scene increase, training NeRF models with a single GPU proves insufficient for achieving fast training and high-quality reconstruction. To address this challenge, prior works proposed distributed NeRF training methods. This is the first work to conduct a detailed evaluation of two major distributed NeRF training methods and their tradeoffs: distributed data parallel (DDP) and spatial segmentation (SS). We find that DDP training requires cross-device synchronization during training, while SS training incurs additional fusion overhead during inference. Our analysis also reveals that sampling input images is a common key bottleneck in distributed NeRF training. At the beginning of each training iteration, the CPU generates input batches for all GPUs in the cluster by sampling all images in the dataset, causing significant stalls that constitute up to 43.3% of the total training time. To alleviate this bottleneck, we propose a pipelined input sampling strategy that precomputes input samples on the CPU concurrently with model training on the GPUs. Our evaluation demonstrates an average speedup in training time by$1.95\times($up to$2.24\times)$.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".