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Tremendous advances in parallel computing and graphics hardware opened upseveral novel real-time GPU applications in the fields of computer vision,computer graphics as well as augmented reality (AR) and virtual reality (VR).Although these applications built upon established open-source frameworks thatprovide highly optimized algorithms, they often come with custom self-writtendata structures to manage the underlying data. In this work, we present stdgpu,an open-source library which defines several generic GPU data structures forfast and reliable data management. Rather than abandoning previous establishedframeworks, our library aims to extend them, therefore bridging the gap betweenCPU and GPU computing. This way, it provides clean and familiar interfaces andintegrates seamlessly into new as well as existing projects. We hope to fosterfurther developments towards unified CPU and GPU computing and welcomecontributions from the community.

stdgpu: Efficient STL-like Data Structures on the GPU