Speculative ray scheduling for large data visualization on supercomputers
| dc.contributor.advisor | Fussell, Donald S., 1951- | |
| dc.contributor.advisor | Navratil, Paul Arthur | |
| dc.contributor.committeeMember | Erez, Mattan | |
| dc.contributor.committeeMember | Garg, Vijay | |
| dc.contributor.committeeMember | Vouga, Etienne | |
| dc.creator | Park, Hyungman | |
| dc.date.accessioned | 2022-05-20T16:45:32Z | |
| dc.date.available | 2022-05-20T16:45:32Z | |
| dc.date.created | 2021-08 | |
| dc.date.issued | 2021-08-16 | |
| dc.date.submitted | August 2021 | |
| dc.date.updated | 2022-05-20T16:45:33Z | |
| dc.description.abstract | Scientific ray tracing now can include realistic shading and material properties, but tracing rays through partitioned data to calculate global illumination is inefficient because of the I/O overhead incurred by rays migrating from one partition to another. For such data, ray scheduling methods have demonstrated improved rendering performance by amortizing costs across a large group of rays. However, ray schedulers are prone to long-tail effects where much time is spent computing the solution for the final few rays, particularly for irregular ray tracing workloads. Solving this long-tail problem is increasingly important to maintain performance as complex ray tracing becomes more common for scientific analysis and for direct simulation of ray-like phenomena. In response, this dissertation introduces the concept of controlled redundancy to the domain of ray scheduling by means of speculation. We demonstrate that for both out-of-core and in situ rendering scenarios, speculatively scheduling rays to different regions of space both increases utilization of underlying resources and reduces total rendering time. In addition, we establish a communication abstraction to form a scheduling framework for novel asynchronous speculation. Furthermore, we incorporate simple heuristic prediction models, making the framework highly adaptable to a spectrum of scene characteristics. The framework is flexible enough to support a wide range of rendering techniques, including many variants of volume rendering and geometry rendering. Facilitated by high utilization, our scheduling method achieves many-times higher throughput on a multi-node, distributed system than prior methods, making our method fit for both interactive and offline applications. | |
| dc.description.department | Electrical and Computer Engineering | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | https://hdl.handle.net/2152/114196 | |
| dc.identifier.uri | http://dx.doi.org/10.26153/tsw/41099 | |
| dc.subject | Distributed-memory ray tracing | |
| dc.subject | Scientific visualization | |
| dc.title | Speculative ray scheduling for large data visualization on supercomputers | |
| dc.type | Thesis | |
| dc.type.material | text | |
| thesis.degree.department | Electrical and Computer Engineering | |
| thesis.degree.discipline | Electrical and Computer Engineering | |
| thesis.degree.grantor | The University of Texas at Austin | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |
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