Efficient processing of k-nearest neighbor (kNN) join operations on streaming data is critical for applications in location-aware services, recommendation systems, and spatial analytics. To serve users in real time, these applications generally require a high-performance kNN join on continuously changing streaming data. This paper introduces StreamingRT, a framework that leverages ray tracing (RT) cores in GPUs to accelerate stream kNN joins in 3D space. By modeling stream data into large primitives and transferring queries into short rays, StreamingRT transforms the kNN join problem into an efficient ray tracing task. To address the ray tracing index updating overhead on stream data, we propose two key techniques, i.e., boundary-extended point partitioning and query-driven BVH lazy updating. Moreover, we also adopt multi-BVH coprocessing and CPU-GPU pipelining to improve performance. These techniques enable efficient stream kNN join on ray tracing cores, delivering unprecedented performance improvement. Experimental evaluations show that StreamingRT can achieve up to 2.2× and 5.8× speedup over the state-of-the-art approach on RT cores and CUDA cores, respectively.

An increasing number of web applications require cloud in-memory key-value stores to minimize latency and achieve higher throughput. They generally have diverse characteristics and constantly changing traffic volumes, which require different computational and memory resources. A serverless in-memory key-value store characterized by elastic resource allocation and pay-as-you-go billing could satisfy the requirements of diverse and dynamic workloads. However, we find current serverless IMKVs fail to achieve fine-grained and prompt resource elasticity due to the limitations of their infrastructures. This paper proposes Genie, a lightweight serverless infrastructure for in-memory key-value caching with fine-grained and immediate elasticity. In Genie, a novel approach is adopted to enable dynamic and independent resource allocation to multiple tenants. It processes all arrived requests and estimates the vCPU consumption with a lightweight machine-learning approach for fine-grained billing. Moreover, Genie estimates the working set and dynamically resizes the allocated memory for hit ratio requirements. Evaluation results show that CPU estimation could be achieved every 100 microseconds without impacting system performance, and memory capacity could be adjusted by megabytes within seconds. The holistic design incurs 1%-2% performance degradation compared to our baseline. Moreover, Genie achieves an average of 58.3% CPU and 49.9% memory savings compared to AsparaDB for Memcache.