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Martin Sattlecker

dblp:172/8721 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0007-7490-4466ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Rendering · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
GPUs and heterogeneous computing · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Rendering › participating media rendering
cloud rendering
0.912025
Sparse Cache Updates for Scalable Distributed Effect-Based Rendering · SIGGRAPH Asia 2025
Rendering › parallel rendering
distributed rendering
0.912025
Sparse Cache Updates for Scalable Distributed Effect-Based Rendering · SIGGRAPH Asia 2025
GPUs and heterogeneous computing › multi-GPU computing
multi-GPU rendering
0.312025
Sparse Cache Updates for Scalable Distributed Effect-Based Rendering · SIGGRAPH Asia 2025

Methods — techniques the papers use, named apart from their topics

sparse cache update · 1.7
YearPublicationVenuePosition
2025 Sparse Cache Updates for Scalable Distributed Effect-Based Rendering
abstract
Cloud 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 (OSC), 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 6 × up to 65 × —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.
Wolfgang Tatzgern, Pascal Stadlbauer, Joerg H. Mueller, Martin Sattlecker, Markus Steinberger
SIGGRAPH Asia5