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Peter D. Kirchner

dblp:40/4867 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2003
—ORCID · none

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

Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-author

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
Distributed systems · 100%

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

TopicWeightPapersLastEvidence papers
Rendering › parallel rendering › distributed rendering
cluster rendering
0.012002
Chromium: a stream-processing framework for interactive rendering on clusters · ACM Trans. Graph. 2002
Rendering
parallel rendering
0.012002
Chromium: a stream-processing framework for interactive rendering on clusters · ACM Trans. Graph. 2002
Distributed systems
stream processing
0.012002
Chromium: a stream-processing framework for interactive rendering on clusters · ACM Trans. Graph. 2002

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

graphics API stream filtering · 0.1
YearPublicationVenuePosition
2003 Scalable visualization using a network-attached video framebuffer
Peter D. Kirchner, James T. Klosowski, Peter Hochschild, Richard A. Swetz
Comput. Graph.1
2002 Chromium: a stream-processing framework for interactive rendering on clusters
abstract
We describe Chromium, a system for manipulating streams of graphics API commands on clusters of workstations. Chromium's stream filters can be arranged to create sort-first and sort-last parallel graphics architectures that, in many cases, support the same applications while using only commodity graphics accelerators. In addition, these stream filters can be extended programmatically, allowing the user to customize the stream transformations performed by nodes in a cluster. Because our stream processing mechanism is completely general, any cluster-parallel rendering algorithm can be either implemented on top of or embedded in Chromium. In this paper, we give examples of real-world applications that use Chromium to achieve good scalability on clusters of workstations, and describe other potential uses of this stream processing technology. By completely abstracting the underlying graphics architecture, network topology, and API command processing semantics, we allow a variety of applications to run in different environments.
Greg Humphreys, Mike Houston, Ren Ng, Randall Frank, Sean Ahern, Peter D. Kirchner, James T. Klosowski
ACM Trans. Graph.6