Evaluating the Impact of Energy Efficient Networks on HPC Workloads Interconnection networks grow larger as supercomputers include more nodes and require higher bandwidth for performance. This scaling significantly increases the fraction of power consumed by the network, by increasing the number of network components (links and switches). Typically, network links consume power continuously once they are turned on. However, recent proposals for energy efficient interconnects have introduced lowpower operation modes for periods when network links are idle. Lowpower operation can increase messaging time when switching a link from lowpower to active operation. We extend the TraceRCODES network simulator for power modeling to evaluate the impact of energy efficient networking on power and performance. Our evaluation presents the first study on both singlejob and multijob execution to realistically simulate power consumption and performance under congestion for a largescale HPC network. Results on several workloads consisting of HPC proxy applications show that singlejob and multijob execution favor different modes of low power operation to have significant power savings at the cost of minimal performance degradation.

Minimizing Communication Overheads in Containerbased Clouds for HPC Applications Although the industry has embraced the cloud computing model, there are still significant challenges to be addressed concerning the quality of cloud services. Networkintensive applications may not scale in the cloud due to the sharing of the network infrastructure. In the literature, performance evaluation studies are showing that the network tends to limit the scalability and performance of HPC applications. Therefore, we proposed the aggregation of Network Interface Cards (NICs) in a readytouse integration with the OpenNebula cloud manager using Linux containers. We perform a set of experiments using a network microbenchmark to get specific network performance metrics and NAS parallel benchmarks to analyze the performance impact on HPC applications. Our results highlight that the implementation of NIC aggregation improves network performance in terms of throughput and latency. Moreover, HPC applications have different patterns of behavior when using our approach, which depends on communication and the amount of data transferring. While networkintensive applications increased the performance up to 38%, other applications with aggregated NICs maintained the same performance or presented slightly worse performance.