A recurrent problem in generating realistic pictures by computers is to represent natural irregular objects and phenomena without undue time or space overhead. We develop a new and powerful solution to this computer graphics problem by modeling objects as sample paths of stochastic processes. Of particular interest are those stochastic processes which previously have been found to be useful models of the natural phenomena to be represented. One such model applicable to the representation of terrains, known as “fractional Brownian motion,” has been developed by Mandelbrot. The value of a new approach to object modeling in computer graphics depends largely on the efficiency of the techniques used to implement the model. We introduce a new algorithm that computes a realistic, visually satisfactory approximation to fractional Brownian motion in faster time than with exact calculations. A major advantage of this technique is that it allows us to compute the surface to arbitrary levels of details without increasing the database. Thus objects with complex appearances can be displayed from a very small database. The character of the surface can be controlled by merely modifying a few parameters. A similar change allows complex motion to be created inexpensively.

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We present a new system for robustly performing Boolean operations on linear, 3D polyhedra. Our system is exact, meaning that all internal numeric predicates are exactly decided in the sense of exact geometric computation. Our BSP-tree based system is 16-28× faster at performing iterative computations than CGAL's Nef Polyhedra based system, the current best practice in robust Boolean operations, while being only twice as slow as the non-robust modeler Maya. Meanwhile, we achieve a much smaller substrate of geometric subroutines than previous work, comprised of only 4 predicates, a convex polygon constructor, and a convex polygon splitting routine. The use of a BSP-tree based Boolean algorithm atop this substrate allows us to explicitly handle all geometric degeneracies without treating a large number of cases.

GPUs employ massive multithreading and fast context switching to provide high throughput and hide memory latency. Multithreading can increase contention for various system resources, however, that may result in suboptimal utilization of shared resources. Previous research has proposed variants of throttling thread-level parallelism to reduce cache contention and improve performance. Throttling approaches can, however, lead to under-utilizing thread contexts, on-chip interconnect, and offchip memory bandwidth. This paper proposes to tightly couple the thread scheduling mechanism with the cache management algorithms such that GPU cache pollution is minimized while offchip memory throughput is enhanced. We propose priority-based cache allocation (PCAL) that provides preferential cache capacity to a subset of high-priority threads while simultaneously allowing lower priority threads to execute without contending for the cache. By tuning thread-level parallelism while both optimizing caching efficiency as well as other shared resource usage, PCAL builds upon previous thread throttling approaches, improving overall performance by an average 17% with maximum 51%. 89 978-1-4799-8930-0/15/$31.00 ©2015 IEEE