Fig. 1. Many real-world volume data sets are sparse, lending appeal to rendering and processing techniques that benefit from such a property. Using our novel, storage-efficient data structure allows us to efficiently store such data sets as bricked textures on the GPU. From left to right: Stagbeetle (832 × 832 × 494, bricksize 7 3 , 5.01% non-empty, 245.5KB index); Pawpawsaurus Campbelli skull (958 × 646 × 1088, bricksize 15 3 , 15.1% non-empty, 50.21KB index); Angiography (416 × 512 × 112, bricksize 3 3 , 2.27% non-empty, 220.5KB index); Present (492 × 492 × 442, bricksize 7 3 , 17.3% non-empty, 78.80KB index). Our data structure introduces only minimal overhead in terms of memory and run-time, and it allows for efficient brick-level empty-space skipping.Abstract-In this paper we present a novel GPU-based data structure for spatial indexing. Based on Fenwick trees-a special type of binary indexed trees-our data structure allows construction in linear time. Updates and prefixes can be computed in logarithmic time, whereas point queries require only constant time on average. Unlike competing data structures such as summed-area tables and spatial hashing, our data structure requires a constant amount of bits for each data element, and it offers unconstrained point queries. This property makes our data structure ideally suited for applications requiring unconstrained indexing of large data, such as block-storage of large and block-sparse volumes. Finally, we provide asymptotic bounds on both run-time and memory requirements, and we show applications for which our new data structure is useful.