GPU上の3DガウシアンスプラッティングにおけるSMレベル負荷不均衡の緩和
Mitigating SM-Level Load Imbalance in 3D Gaussian Splatting on GPUs
3DGSレンダリングでタイル間の負荷偏りが生む実行テールを、重いタイルを先に処理し外れ値を分割する永続カーネルで解消し、再学習なしでアルファブレンディングを最大5.6倍高速化した。
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著者: Junha Ryu, Hoi-Jun Yoo
分類: cs.DC, cs.CV, cs.GR, cs.LG
原文アブストラクト
3D Gaussian Splatting (3DGS) enables photorealistic, real-time rendering on commodity GPUs and is increasingly adopted in generative world models and autonomous driving simulators. However, existing optimizations rely on algorithmic changes that often require retraining, or on specialized accelerators that lack flexibility across evolving 3DGS variants. In this paper, we identify a fundamental inefficiency in GPU-based 3DGS rendering: severe SM-level load imbalance caused by uneven workloads across screen tiles. This imbalance creates a long execution tail, leaving most SMs idle while a few process computationally intensive tiles, particularly in large-scale urban scenes. To address this, we present a software-defined GPU scheduling approach that mitigates the execution tail. Our approach uses a persistent kernel to schedule heavy tiles first and selectively chunks outlier tiles across multiple CTAs. A lightweight per-frame policy determines when and how much to chunk using tile statistics already available in the rendering pipeline. Our approach requires neither retraining nor specialized hardware and can be applied orthogonally to existing optimizations across diverse 3DGS variants. Averaged per scene, it accelerates alpha blending by up to 2.93x on a large-scale urban scene, and by up to 5.6x on its most imbalanced frames, while retaining the original alpha blending formulation.