Recent studies of irregular applications such as finite-element mesh generators and data-clustering codes have shown that these applications have a generalized data parallelism arising from the use of iterative algorithms that perform computations on elements of worklists. In some irregular applications, the computations on different elements are independent. In other applications, there may be complex patterns of dependences between these computations.

Recent application studies have shown that many irregular applications have a generalized data parallelism that manifests itself as iterative computations over worklists of different kinds. In general, there are complex dependencies between iterations. These dependencies cannot be elucidated statically because they depend on the inputs to the program; thus, optimistic parallel execution is the only tractable approach to parallelizing these applications.

Aggregates of individual objects, such as forests, crowds, and piles of fruit, are a common source of complexity in computer graphics scenes. When viewing an aggregate, observers attend less to individual objects and focus more on overall properties such as numerosity, variety, and arrangement. Paradoxically, rendering and modeling costs increase with aggregate complexity, exactly when observers are attending less to individual objects. In this paper we take some first steps to characterize the limits of visual coding of aggregates to efficiently represent their appearance in scenes. We describe psychophysical experiments that explore the roles played by the geometric and material properties of individual objects in observers' abilities to discriminate different aggregate collections. Based on these experiments we derive metrics to predict when two aggregates have the same appearance, even when composed of different objects. In a follow-up experiment we confirm that these metrics can be used to predict the appearance of a range of realistic aggregates. Finally, as a proof-of-concept we show how these new aggregate perception metrics can be applied to simplify scenes by allowing substitution of geometrically simpler aggregates for more complex ones without changing appearance.

Irregular applications, which manipulate large, pointer-based data structures like graphs, are difficult to parallelize manually. Automatic tools and techniques such as restructuring compilers and run-time speculative execution have failed to uncover much parallelism in these applications, in spite of a lot of effort by the research community. These difficulties have even led some researchers to wonder if there is any coarse-grain parallelism worth exploiting in irregular applications.

Efficient, realistic rendering of complex scenes is one of the grand challenges in computer graphics. Perceptually based rendering addresses this challenge by taking advantage of the limits of human vision. However, existing methods, based on predicting visible image differences, are too conservative because some kinds of image differences do not matter to human observers. In this paper, we introduce the concept of visual equivalence , a new standard for image fidelity in graphics. Images are visually equivalent if they convey the same impressions of scene appearance, even if they are visibly different. To understand this phenomenon, we conduct a series of experiments that explore how object geometry, material, and illumination interact to provide information about appearance, and we characterize how two kinds of transformations on illumination maps (blurring and warping) affect these appearance attributes. We then derive visual equivalence predictors (VEPs): metrics for predicting when images rendered with transformed illumination maps will be visually equivalent to images rendered with reference maps. We also run a confirmatory study to validate the effectiveness of these VEPs for general scenes. Finally, we show how VEPs can be used to improve the efficiency of two rendering algorithms: Light-cuts and precomputed radiance transfer. This work represents some promising first steps towards developing perceptual metrics based on higher order aspects of visual coding.

This paper describes CMS (constrained minimization synthesis), a fast, robust texture synthesis algorithm that creates output textures while satisfying constraints. We show that constrained texture synthesis can be posed in a principled way as an energy minimization problem that requires balancing two measures of quality: constraint satisfaction and texture seamlessness. We then present an efficient algorithm for finding good solutions to this problem using an adaptation of graphcut energy minimization. CMS is particularly well suited to detail synthesis, the process of adding high-resolution detail to low-resolution images. It also supports the full Image Analogies framework, while providing superior image quality and performance. CMS is easily extended to handle multiple constraints on a single output, thus enabling novel applications that combine both user-specified and image-based control.