Scalable Breadth-First Search on a GPU Cluster

On a GPU cluster, the ratio of high computing power to communication bandwidth makes scaling breadth-first search (BFS) on a scale-free graph extremely challenging. By separating high and low out-degree vertices, we present an implementation with scalable computation and a model for scalable communication for BFS and direction-optimized BFS. Our communication model uses global reduction for high-degree vertices, and point-to-point transmission for low-degree vertices. Leveraging the characteristics of degree separation, we reduce the graph size to one third of the conventional edge list representation. With several other optimizations, we observe linear weak scaling as we increase the number of GPUs, and achieve 259.8 GTEPS on a scale-33 Graph500 RMAT graph with 124 GPUs on the latest CORAL early access system.Comment: 12 pages, 13 figures. To appear at IPDPS 201

ENERGY AWARE TRAFFIC ENGINEERING IN WIRED COMMUNICATION NETWORKS

The reduction of power consumption in communication networks has become a key issue for both the Internet Service Providers (ISP) and the research community. Ac- cording to different studies, the power consumption of Information and Communication Technologies (ICT) varies from 2% to 10% of the worldwide power consumption [1,2]. Moreover, the expected trends for the future predict a notably increase of the ICT power consumption, doubling its value by 2020 [2] and growing to around 30% of the worldwide electricity demand by 2030 according to business-as-usual evaluation scenarios [15]. It is therefore not surprising that researchers, manufacturers and network providers are spending significant efforts to reduce the power consumption of ICT systems from dif- ferent angles. To this extent, networking devices waste a considerable amount of power. In partic- ular, their power consumption has always been increased in the last years, coupled with the increase of the offered performance [16]. Actually, power consumption of network- ing devices scales with the installed capacity, rather than the current load [17]. Thus, for an ISP the network power consumption is practically constant, unrespectively to traffic fluctuations. However, actual traffic is subject to strong day/night oscillations [3]. Thus, many devices are underutilized, especially during off-peak hours when traffic is low. This represents a clear opportunity for saving energy, since many resources (i.e., routers and links) are powered on without being fully utilized. In this context, resource consolidation is a known paradigm for the reduction of the power consumption. It consists in having a carefully selected subset of network devices entering a low power state, and use the rest to transport the required amount of traffic. This is possible without disrupting the Quality of Service (QoS) offered by the network infrastructure, since communication networks are designed over the peak foreseen traffic request, and with redundancy and over-provisioning in mind. In this thesis work, we present different techniques to perform resource consolida- tion in backbone IP-based networks, ranging from centralized solutions, where a central entity computes a global solution based on an omniscient vision of the network, to dis- tributed solutions, where single nodes take independent decisions on the local power- state, based solely on local knowledge. Moreover, different technological assumptions are made, to account for different possible directions of the network devices evolutions, ranging from the possibility to switch off linecard ports, to whole network nodes, and taking into account different power consumption profiles

Demand Response for Residential Appliances in a Smart Electricity Distribution Network: Utility and Customer Perspectives

This thesis introduces advanced Demand Response algorithms for residential appliances to provide benefits for both utility and customers. The algorithms are engaged in scheduling appliances appropriately in a critical peak day to alleviate network peak, adverse voltage conditions and wholesale price spikes also reducing the cost of residential energy consumption. Initially, a demand response technique via customer reward is proposed, where the utility controls appliances to achieve network improvement. Then, an improved real-time pricing scheme is introduced and customers are supported by energy management schedulers to actively participate in it. Finally, the demand response algorithm is improved to provide frequency regulation services