Performance characterization and acceleration of big data workloads on OpenPOWER system

Abstract

IBM's POWER processor has been advocated as the high-performance architecture designed for processing Big Data workloads. With the collaborations through the OpenPOWER Foundation, more and more innovations for POWER architecture are emerging to solve Big Data challenges. For example, with the cooperation between IBM and Mellanox, the latest generation of Remote Direct Memory Access (RDMA) capable InfiniBand network can deliver tremendous performance on POWER processors. On the other hand, many RDMA-based designs and optimizations recently have been proposed in the community for accelerating big data processing systems (such as Apache Hadoop and Spark). However, these studies mostly focus on achieving higher performance over Intel Xeon or other x86 architectures. As OpenPOWER systems are getting momentum, we set out to answer the question how much can the RDMA-based communication runtime benefit Big Data processing middleware running over OpenPOWER systems as compared to the default TCP/IP-based designs. To answer this question, this paper first presents an extensive performance characterization on RDMA-based Hadoop RPC engine over OpenPOWER system. We further propose new designs to enable efficient CPU affinity policies and architecture-aware tuning in the RDMA-based communication engine for Hadoop and Spark. With these various accelerations, our performance evaluation shows that our proposed designs can achieve up to 2.73X performance improvement for Hadoop RPC benchmark as compared to default Hadoop running with IP-over-IB protocol on OpenPOWER systems. In addition, our proposed design can gain up to 29.37% performance improvement for Hadoop and Spark workloads as compared to the default RDMA designs running on an OpenPOWER cluster.