Out‐of‐Core and Dynamic Programming for Data Distribution on a Volume Visualization Cluster

Abstract

Ray directed volume-rendering algorithms are well suited for parallel implementation in a distributed cluster environment. For distributed ray casting, the scene must be partitioned between nodes for good load balancing, and a strict view-dependent priority order is required for image composition. In this paper, we define the load balanced network distribution (LBND) problem and map it to the NP-complete precedence constrained job-shop scheduling problem. We introduce a kd-tree solution and a dynamic programming solution. To process a massive data set, either a parallel or an out-of-core approach is required. Parallel preprocessing is performed by render nodes on data, which are allocated using a static data structure. Volumetric data sets often contain a large portion of voxels that will never be rendered, or empty space. Parallel preprocessing fails to take advantage of this. Our slab-projection slice, introduced in this paper, tracks empty space across consecutive slices of data to reduce the amount of data distributed and rendered. It is used to facilitate out-of-core bricking and kd-tree partitioning. Load balancing using each of our approaches is compared with traditional methods using several segmented regions of the Visible Korean data set.

A Study of Parallel Particle Tracing for Steady-State and Time-Varying Flow Fields

“…Moloney et al [20] used dynamic load balancing in sortfirst parallel volume rendering to accurately predict the amount of imbalance across eight rendering nodes. Frank and Kaufman [21] used dynamic programming to perform load balancing in sort-last parallel volume rendering across 64 nodes. Marchesin et al [22] and Müller et al [23] used a KD-tree for object space partitioning in parallel volume rendering, and Lee et al [24] used an octree and parallel BSP tree to load-balance GPU-based volume rendering.…”

GPU-based out-of-core many-lights rendering

“…Dietrich et al [2007] and Gobbetti et al [2008] provided good overviews and surveys on out-of-core rendering techniques and strategies. Frank and Kaufman [2009] proposed an out-of-core volume visualization method that also uses the graph to manage the out-of-core workload. However, their problem was formulated and solved differently in different applications with ours.…”