Uploaded July 2013 | Updated September 2026, 1 week ago
CloudLight, a system for computing indirect lighting in the Cloud to support real-time rendering for interactive 3D applications on a user's local device. CloudLight maps the traditional graphics pipeline onto a distributed system. That differs from a single-machine renderer in three fundamental ways.
First, the mapping introduces potential asymmetry between computational resources available at the Cloud and local device sides of the pipeline.
Second, compared to a hardware memory bus, the network introduces relatively large latency and low bandwidth between certain pipeline stages.
Third, for multi-user virtual environments, a Cloud solution can amortize expensive global illumination costs across users. Our new CloudLight framework explores tradeoffs in different partitions of the global illumination workload between Cloud and local devices, with an eye to how available network and computational power influence design decisions and image quality. We describe the tradeoffs and characteristics of mapping three known lighting algorithms to our system and demonstrate scaling for up to 50 simultaneous CloudLight users.
CloudLight, a system for computing indirect lighting in the Cloud to support real-time rendering for interactive 3D applications on a user's local device. CloudLight maps the traditional graphics pipeline onto a distributed system. That differs from a single-machine renderer in three fundamental ways.
First, the mapping introduces potential asymmetry between computational resources available at the Cloud and local device sides of the pipeline.
Second, compared to a hardware memory bus, the network introduces relatively large latency and low bandwidth between certain pipeline stages.
Third, for multi-user virtual environments, a Cloud solution can amortize expensive global illumination costs across users. Our new CloudLight framework explores tradeoffs in different partitions of the global illumination workload between Cloud and local devices, with an eye to how available network and computational power influence design decisions and image quality. We describe the tradeoffs and characteristics of mapping three known lighting algorithms to our system and demonstrate scaling for up to 50 simultaneous CloudLight users.
