thesis
High-fidelity rendering on shared computational resources
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Abstract
The generation of high-fidelity imagery is a computationally expensive process
and parallel computing has been traditionally employed to alleviate this cost.
However, traditional parallel rendering has been restricted to expensive shared
memory or dedicated distributed processors. In contrast, parallel computing on
shared resources such as a computational or a desktop grid, offers a low cost alternative. But, the prevalent rendering systems are currently incapable of seamlessly handling such shared resources as they suffer from high latencies, restricted
bandwidth and volatility. A conventional approach of rescheduling failed jobs in
a volatile environment inhibits performance by using redundant computations.
Instead, clever task subdivision along with image reconstruction techniques provides an unrestrictive fault-tolerance mechanism, which is highly suitable for
high-fidelity rendering. This thesis presents novel fault-tolerant parallel rendering algorithms for effectively tapping the enormous inexpensive computational
power provided by shared resources.
A first of its kind system for fully dynamic high-fidelity interactive rendering
on idle resources is presented which is key for providing an immediate feedback
to the changes made by a user. The system achieves interactivity by monitoring
and adapting computations according to run-time variations in the computational
power and employs a spatio-temporal image reconstruction technique for enhancing the visual fidelity. Furthermore, algorithms described for time-constrained offline rendering of still images and animation sequences, make it possible to deliver
the results in a user-defined limit. These novel methods enable the employment
of variable resources in deadline-driven environments