This study presents an approach to collect and classify usage data of public charging infrastructure in order to predict usage based on socio-demographic data within a city. The approach comprises data acquisition and a two-step machine learning approach, classifying and predicting usage behavior. Data is acquired by gathering information on charging points from publicly available sources. The first machine learning step identifies four relevant usage patterns from the gathered data using an agglomerative clustering approach. The second step utilizes a Random Forest Classification to predict usage patterns from socio-demographic factors in a spatial context. This approach allows to predict usage behavior at locations for potential new charging points. Applying the presented approach to Munich, a large city in Germany, results confirm the adaptability in complex urban environments. Visualizing the spatial distribution of the predicted usage patterns shows the prevalence of different patterns throughout the city. The presented approach helps municipalities and charging infrastructure operators to identify areas with certain usage patterns and, hence different technical requirements, to optimize the charging infrastructure in order to help meeting the increasing demand of electric mobility.

Fully autonomously driving vehicles are expected to be a widely available technology in the near future. Privately owned cars, which remain parked for the majority of their lifetime, may therefore be capable of driving independently during their usual long parking periods (e.g. their owners working hours). Our analysis aims to focus on the potential of a privately owned shared car concept as transition period between the present usages of privately owned cars towards a transportation paradigm of privately owned shared autonomous vehicles. We propose two methods in the field of reachability analysis to evaluate the impact of such vehicles during parking periods. Our proposed methods are applied to a dataset of parking times of users of a telematics service provider in the Munich area (Germany). We show the impact of time and location dependent effects on the analyzed service coverage, such as business week rush hours or cover age divergence between urban and suburban regions.

The continuously rising number of cores per socket puts a growing demand on on-chip interconnects. The topology of these interconnects are largely kept hidden from the user, yet, they can be the source of measurable performance differences for large many-core processors due to core placement on that interconnect. This paper investigates the ARM Coherent Mesh Network (CMN) on an Ampere Altra Max processor. We provide novel insights into the interconnect by experimentally deriving key information on the CMN topology, such as the position of cores or memory and cache controllers. Based on this insight, we evaluate the performance characteristics of several benchmarks and tune the thread-to-core mapping to improve application performance. Our methodology is directly applicable to all ARM-based processors using the ARM CMN, but in principle applies to all mesh-based on-chip networks.

The network hardware used in High-Performance Computing (HPC) systems is one of the core differentiators to regular compute clusters and has seen substantial improvements over the years. However, to fully utilize its capabilities, careful application tuning is necessary, which in turn is only possible if insights into application behavior can be obtained. With the rise of increasingly complex HPC application software stacks, this requires new kinds of fine-grained network monitoring capabilities. While regular TCP/IP based traffic can be monitored using a large variety of existing tools, for Remote Direct Memory Access (RDMA) based communication, which is prevalent in HPC networks, only very few tools are available. Further, those few tools either largely depend on features of specific network hardware, are mostly limited to node-granular monitoring only, or are limited to specific programming models. In this paper, we introduce a novel network monitoring tool that directly layers on a portable network abstraction library, namely libfabric, while enabling application specific monitoring. With that, our tool provides network hardware and programming model agnostic, per-process monitoring of RDMA-based network utilization and is capable of always-on, system-wide monitoring of production environments. We conduct a detailed overhead analysis on several state-of-the-art HPC systems with a variety of network hardware and show that our monitoring tool induces low overhead in the monitored application. In addition, we apply our monitoring tool to a real-world HPC application running in a production environment.