Field Trial of a Flexible Real-time Software-defined GPU-based Optical Receiver

We introduce a flexible, software-defined real-time multi-modulation format receiver implemented on an off-the-shelf general-purpose graphics processing unit (GPU). The flexible receiver is able to process 2 GBaud 2-, 4-, 8-, and 16-ary pulse-amplitude modulation (PAM) signals as well as 1 GBaud 4-, 16- and 64-ary quadrature amplitude modulation (QAM) signals, with the latter detected using a Kramers-Kronig (KK) coherent receiver. Experimental performance evaluation is shown for back-to-back. In addition, by using the JGN high speed R&D network testbed, performance is evaluated after transmission over 91 km field-deployed optical fiber and reconfigurable optical add-drop multiplexers (ROADMs).Comment: Accepted for publication at Journal of Lightwave Technology, already available via JLT Early Access, see supplied DOI. This v2 version of the article is improved w.r.t. v1 after JLT peer-review. This article is a longer journal version of the conference paper: S.P. van der Heide, et al., Real-time, Software-Defined, GPU-Based Receiver Field Trial, ECOC 2020 paper We1E5, also via arXiv:2010.1433

The Bionic DBMS is Coming, but What Will It Look Like?

Software has always ruled database engines, and commodity processors riding Moore's Law doomed database machines of the 1980s from the start. However, today's hardware landscape is very different, and moving in directions that make database machines increasingly attractive. Stagnant clock speeds, looming dark silicon, availability of reconfigurable hardware, and the economic clout of cloud providers all align to make custom database hardware economically viable or even necessary. Dataflow workloads (business intelligence and streaming) already benefit from emerging hardware support. In this paper, we argue that control flow workloads with their corresponding latencies are another feasible target for hardware support. To make our point, we outline a transaction processing architecture that offloads much of its functionality to reconfigurable hardware. We predict a convergence to fully "bionic" database engines that implement nearly all key functionality directly in hardware and relegate software to a largely managerial role