Presentation
Sparse Cache Updates for Scalable Distributed Effect-Based Rendering
DescriptionCloud computing has seen rapid growth in recent years, accompanied by the increasing popularity of game streaming services that allow users to play high-end games on low-end devices, across platforms, and from virtually anywhere.
The rise of multiplayer games, shared immersive experiences, and metaverse-style applications—such as exhibitions or social virtual spaces—presents unique opportunities for improving rendering efficiency.
In particular, the presence of multiple viewers within the same virtual environment opens the door for computation reuse across rendering instances.
We propose a scalable, multi-GPU cloud rendering system tailored for multi-viewer scenarios.
Built on top of on-surface caches, our system extends the core idea of decoupling shading from viewpoints to enable efficient reuse of shading information across multiple users.
Our system is designed to scale with an increasing number of viewers by dynamically distributing rendering workloads across multiple GPUs.
We further enhance scalability and significantly reduce inter-GPU bandwidth requirements from 6x up to 65x—through a novel sparse cache update strategy.
Instead of copying full frames between GPUs, our method selectively propagates only relevant cache updates, enabling efficient data sharing while minimizing redundant transfers.
The rise of multiplayer games, shared immersive experiences, and metaverse-style applications—such as exhibitions or social virtual spaces—presents unique opportunities for improving rendering efficiency.
In particular, the presence of multiple viewers within the same virtual environment opens the door for computation reuse across rendering instances.
We propose a scalable, multi-GPU cloud rendering system tailored for multi-viewer scenarios.
Built on top of on-surface caches, our system extends the core idea of decoupling shading from viewpoints to enable efficient reuse of shading information across multiple users.
Our system is designed to scale with an increasing number of viewers by dynamically distributing rendering workloads across multiple GPUs.
We further enhance scalability and significantly reduce inter-GPU bandwidth requirements from 6x up to 65x—through a novel sparse cache update strategy.
Instead of copying full frames between GPUs, our method selectively propagates only relevant cache updates, enabling efficient data sharing while minimizing redundant transfers.
Event Type
Technical Papers
TimeWednesday, 17 December 20253:33pm - 3:44pm HKT
LocationMeeting Room S426+S427, Level 4