Abstract. Active data structures support operations that may affect a large number of elements of an aggregate data structure. They are well suited for extremely fine grain parallel systems, including circuit parallelism. General purpose GPUs were designed to support regular graphics algorithms, but their intermediate level of granularity makes them potentially viable also for active data structures. We consider the characteristics of active data structures and discuss the feasibility of implementing them on GPGPUs. We describe the GPU implementations of two such data structures, extensible sparse functional (ESF) arrays and index intervals. We measure their performance and discuss the potential of active data structures as an unconventional programming model that can exploit the capabilities of emerging fine grain architectures such as GPUs.