Network-Compute Co-Design for Distributed In-Memory Computing

The booming popularity of online services is rapidly raising the demands for modern datacenters. In order to cope with data deluge, growing user bases, and tight quality of service constraints, service providers deploy massive datacenters with tens to hundreds of thousands of servers, keeping petabytes of latency-critical data memory resident. Such data distribution and the multi-tiered nature of the software used by feature-rich services results in frequent inter-server communication and remote memory access over the network. Hence, networking takes center stage in datacenters. In response to growing internal datacenter network traffic, networking technology is rapidly evolving. Lean user-level protocols, like RDMA, and high-performance fabrics have started making their appearance, dramatically reducing datacenter-wide network latency and offering unprecedented per-server bandwidth. At the same time, the end of Dennard scaling is grinding processor performance improvements to a halt. The net result is a growing mismatch between the per-server network and compute capabilities: it will soon be difficult for a server processor to utilize all of its available network bandwidth. Restoring balance between network and compute capabilities requires tighter co-design of the two. The network interface (NI) is of particular interest, as it lies on the boundary of network and compute. In this thesis, we focus on the design of an NI for a lightweight RDMA-like protocol and its full integration with modern manycore server processors. The NI capabilities scale with both the increasing network bandwidth and the growing number of cores on modern server processors. Leveraging our architecture's integrated NI logic, we introduce new functionality at the network endpoints that yields performance improvements for distributed systems. Such additions include new network operations with stronger semantics tailored to common application requirements and integrated logic for balancing network load across a modern processor's multiple cores. We make the case that exposing richer, end-to-end semantics to the NI is a unique enabler for optimizations that can reduce software complexity and remove significant load from the processor, contributing towards maintaining balance between the two valuable resources of network and compute. Overall, network-compute co-design is an approach that addresses challenges associated with the emerging technological mismatch of compute and networking capabilities, yielding significant performance improvements for distributed memory systems

Applications in Electronics Pervading Industry, Environment and Society

This book features the manuscripts accepted for the Special Issue “Applications in Electronics Pervading Industry, Environment and Society—Sensing Systems and Pervasive Intelligence” of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the “Applications in Electronics Pervading Industry, Environment and Society” (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs