Ray tracing large distributed datasets using ray caches

Many large scale simulations now produce datasets that are signi cantly larger than can typically be stored in memory on a visualization system. Visualization algorithms then become ine ective and stall since the data must be paged to disk. Recently, in-situ visualization has received renewed attention for visualizing large datasets that are distributed among many processors during a simulation. It takes advantage of the fact that the full dataset is already in main memory, distributed among multiple processors. Visualization in this environment then requires communication which can be more expensive than disk access. The goal of this thesis was to develop an in-situ visualization technique using ray tracing that employs ray caches to reduce communication overhead. Ray caches attempt to replace a communication operation with a less expensive cache search operation. A prototype implemented on Sharcnet shows ray caching can signi cantly improve overall performance at a small cost to image quality.

Name thesis:degree_name

Master of Science (MSc)

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2012

Author dc:creator

• Little, Christopher

Advisors dc:contributor.advisor

• Green, Mark
• Qureshi, Faisal

• Visualization
• Cluster
• Sharcnet
• Simulation
• In-situ

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/212

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/212