Transparent network acceleration for big data computing in Java

HPC and cloud data centers offer an increasing amount of cores per CPU, GPUs and high-speed networks like InfiniBand with up to 400 Gbit/s. Scaling out big-data computing is mostly limited by the network performance. However, many big data frameworks are written in Java (often using netty), which cannot fully exploit the performance of such networks. The reason is found in Java NIO, which is based on traditional sockets, while InfiniBand provides ibverbs, a totally different interface, to the operating system and applications. This challenge has been addressed by different approaches, providing transparent and non-transparent acceleration via high-speed NICs, many of them no longer maintained.In this paper, we propose hadroNIO, a Java library, providing transparent network acceleration for Java NIO applications, based on Unified Communication X (UCX). The latter is written in C, providing efficient access to different network technologies. hadroNIO has been extended to use Infinileap for efficiently accessing UCX. Infinileap is using the new Foreign Function & Memory APIs of Oracle’s Project Panama to access native code.Our evaluation results show, that hadroNIO allows netty to achieve round-trip times of less than 5 μs on a 100 GBit/s network and efficiently handle hundreds of connections per server. We compare the raw performance of hadroNIO with traditional Java sockets and libvma using a netty microbenchmark and include experiments with gRPC and Apache ZooKeeper. The measurements show, that hadroNIO outperforms existing solutions, while being transparent for applications and developers.