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Ontario Tech acknowledges the lands and people of the Mississaugas of Scugog Island First Nation.

We are thankful to be welcome on these lands in friendship. The lands we are situated on are covered by the Williams Treaties and are the traditional territory of the Mississaugas, a branch of the greater Anishinaabeg Nation, including Algonquin, Ojibway, Odawa and Pottawatomi. These lands remain home to many Indigenous nations and peoples.

We acknowledge this land out of respect for the Indigenous nations who have cared for Turtle Island, also called North America, from before the arrival of settler peoples until this day. Most importantly, we acknowledge that the history of these lands has been tainted by poor treatment and a lack of friendship with the First Nations who call them home.

This history is something we are all affected by because we are all treaty people in Canada. We all have a shared history to reflect on, and each of us is affected by this history in different ways. Our past defines our present, but if we move forward as friends and allies, then it does not have to define our future.

Learn more about Indigenous Education and Cultural Services

November 30, 2011

Speaker: Mr. Christopher Little, MSc Student, Computer Science, Ontario Tech 

Title: Ray Tracing Large Distributed Datasets Using Ray Caches

Abstract: Ray tracing is extremely useful for visualization due to its high quality and high accuracy. Most large scale simulations now produce datasets that can be significantly larger than can typically be stored in memory on a visualization system. Visualizing datasets of this size with a typical ray tracing algorithm becomes ineffective and stalls 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. In-situ visualization takes advantage of the fact that each processor taking part in the simulation will already have a portion of the dataset loaded into memory. To render this distributed dataset, however, then requires communication between the processors which can be just as expensive as disk access if it is not managed efficiently. The goal of this thesis was to develop an in-situ visualization technique using distributed out-of-core ray tracing. This technique assumes that each processor in a cluster contains a subset of the simulation dataset. To ray trace the dataset, rays traverse the processors which perform a local ray tracing algorithm with the data subset that it stores. The number of rays that pass between processors is often quite large, which causes significant communication overhead. To alleviate this, ray caches are placed at the boundaries between processors to capture and reuse rays, thereby replacing communication with a significantly less expensive cache search operation. Through testing of a simple implementation based on ray casting it was found that ray caching can significantly improve overall performance at a small cost to image quality.

Biography: Christopher Little is currently pursuing his MSc in Computer Science at Ontario Tech under the supervision of Dr. Mark Green and Dr. Faisal Qureshi. He is expected to graduate in the next few months. Christopher received his BSc (Hons.) in Computing Science from Ontario Tech as part of the first ever Computing Science graduating class in 2009.