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Networkswith Remote DirectMemoryAccess (RDMA) support are becoming increasingly common. RDMA, however, offers a limited programming interface to remote memory that consists of read, write and atomic operations. With RDMA alone, completing the most basic operations on remote data structures often requires multiple round-trips over the network. Data-intensive systems strongly desire higher-level communication abstractions that supportmore complex interaction patterns. A natural candidate to consider is MPI, the de facto standard for developing high-performance applications in the HPC community. This paper critically evaluates the communication primitives of MPI and shows that using MPI in the context of a data processing system comes with its own set of insurmountable challenges. Based on this analysis, we propose a new communication abstraction named RDMO, or Remote DirectMemory Operation, that dispatches a short sequence of reads, writes and atomic operations to remote memory and executes them in a single round-trip. 

Sampling is often used to reduce query latency for interactive big data analytics. The established parallel data processing paradigm relies on function shipping, where a coordinator dispatches queries to worker nodes and then collects the results. The commoditization of high-performance networking makes data shipping possible, where the coordinator directly reads data in the workers’ memory using RDMA while workers process other queries. In this work, we explore when to use function shipping or data shipping for interactive query processing with sampling. Whether function shipping or data shipping should be preferred depends on the amount of data transferred, the current CPU utilization and the sampling method. The results show that data shipping is up to 6.5× faster when performing clustered sampling with heavily-utilized workers.