Abstract We present a new multithreaded implementation for the computationally demanding direct volume rendering (DVR) of volumetric data sets on desktop multicore processors using ray casting. The new implementation achieves interactive rendering of very large volumes, even on high resolution screens. Our implementation is based on a new algorithm that combines an object‐order traversal of the volumetric data followed by a focused ray casting. Using a very compact data structure, our method starts with a quick association of data subcubes with fine‐grain screen tiles appearing along the viewing direction in front‐to‐back order. The next stage uses very limited ray casting on the generated sets of subcubes while skipping empty or transparent space and applying early ray termination in an effective way. Our multithreaded implementation makes use of new dynamic techniques to ensure effective memory management and load balancing. Our software enables a user to interactively explore large data sets through DVR while arbitrarily specifying a 2D transfer function. We test our system on a wide variety of well‐known volumetric data sets on a two‐processor Clovertown platform, each consisting of a Quad‐Core 1.86 GHz Intel Xeon Processor. Our experimental tests demonstrate DVR at interactive rates for the largest data sets that can fit in the main memory on our platform. These tests also indicate a high degree of scalability, excellent load balancing, and efficient memory management across the data sets used. Copyright © 2009 John Wiley & Sons, Ltd.

We present a new method for the interactive rendering of isosurfaces using ray casting on multicore processors. This method consists of a combination of an object-order traversal that coarsely identifies possible candidate three-dimensional (3D) data blocks for each small set of contiguous pixels and an isosurface ray casting strategy tailored for the resulting limited-size lists of candidate 3D data blocks. Our implementation scheme results in a compact indexing structure and makes careful use of multithreading and memory management environments commonly present in multicore processors. Although static screen partitioning is widely used in the literature, our scheme starts with an image partitioning for the initial stage and then performs dynamic allocation of groups of ray casting tasks among the different threads to ensure almost equal loads among the different cores while maintaining spatial locality. We also pay a particular attention to the overhead incurred by moving the data across the different levels of the memory hierarchy. We test our system on a two-processor Clovertown platform, each consisting of a Quad-Core 1.86-GHz Intel Xeon Processor and present detailed experimental results for a number of widely different benchmarks. We show that our system is efficient and scalable and achieves high cache performance and excellent load balancing, resulting in an overall performance that is superior to any of the previous algorithms. In fact, we achieve interactive isosurface rendering on a screen with 1.0242 resolution for all the data sets tested up to the maximum size that can fit in the main memory of our platform.