Abstract-High-performance computing (HPC) is recognized as one of the pillars for further advance of science, industry, medicine, and education. Current HPC systems are being developed to overcome emerging challenges in order to reach Exascale level of performance, which is expected by the year 2020. The much larger embedded and mobile market allows for rapid development of IP blocks, and provides more flexibility in designing an application-specific SoC, un turn giving possibility in balancing performance, energy-efficiency and cost. In the Mont-Blanc project, we advocate for HPC systems be built from such commodity IP blocks, currently used in embedded and mobile SoCs.As a first demonstrator of such approach, we present the MontBlanc prototype; the first HPC system built with commodity SoCs, memories, and NICs from the embedded and mobile domain, and offthe-shelf HPC networking, storage, cooling and integration solutions. We present the system's architecture, and evaluation including both performance and energy efficiency. Further, we compare the system's abilities against a production level supercomputer. At the end, we discuss parallel scalability, and estimate the maximum scalability point of this approach across a set of applications.

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SUMMARYThe Graph500 benchmark attempts to steer the design of High-Performance Computing systems to maximize the performance under memory-constricted application workloads. A realistic simulation of such benchmarks for architectural research is challenging due to size and detail limitations. By contrast, synthetic traffic workloads constitute one of the least resource-consuming methods to evaluate the performance. In this work, we provide a simulation tool for network architects that need to evaluate the suitability of their interconnect for BigData applications. Our development is a low computation-and memory-demanding synthetic traffic model that emulates the behavior of the Graph500 communications, and is publicly available in an open-source network simulator. The characterization of network traffic is inferred from a profile of several executions of the benchmark with different input parameters. We verify the validity of the equations in our model against an execution of the benchmark with a different set of parameters. Furthermore, we identify the impact of the node computation capabilities and network characteristics in the execution time of the model in a Dragonfly network.

Low-diameter networks require non-minimal adaptive routing to deal with varying traffic characteristics and avoid pathological performance. Such routing is based on local estimations of network congestion, based on link-level flow control credits. Dragonfly networks based on the extensions of commodity Ethernet networks using OpenFlow have been proposed for large HPC deployments with low power consumption. However, this network technology does not implement credit-based flow control. This work explores a range of routing solutions based on exploiting explicit congestion notification messages (in particular, 802.1Qau) to adapt the number of packets using non-minimal paths. The design (denoted QCN-Switch) associates a probability value to each output port. This value is updated to reflect downstream congestion and used to statistically divert traffic away from congested areas when the load is uneven, as in the case of adversarial traffic. A feedback comparison variant is designed to separate the cases of uniform traffic at saturation and adversarial traffic at low loads. Evaluation results show that QCN-Switch is a competitive design for both the uniform traffic and adversarial traffic. Furthermore, it is able to react to changes in traffic conditions in 0.4 ms or less. A sensitivity analysis identifies the best configuration and shows its performance trade-offs.