. Abstract can be read here. Copyright belongs to IEEE.Abstract-Big Data applications have gained importance over the last few years. Such applications focus on the analysis of huge amounts of unstructured information and present a series of differences with traditional High Performance Computing (HPC) applications. For illustrating such dissimilarities, this paper analyzes the behavior of the most scalable version of the Graph500 benchmark when run on a state-of-the-art commodity cluster facility. Our work shows that this new computation paradigm stresses the interconnection subsystem.In this work, we provide both analytical and empirical characterizations of the Graph500 benchmark, showing that its communication needs bound the achieved performance on a cluster facility. Up to our knowledge, our evaluation is the first to consider the impact of message aggregation on the communication overhead and explore a tradeoff that diminishes benchmark execution time, increasing system performance.

As BigData applications have gained momentum over the last years, the Graph500 benchmark has appeared in an attempt to steer the design of HPC systems to maximize the performance under memoryconstricted application workloads. A realistic simulation of such benchmarks for architectural research is challenging due to size and detail limitations, and synthetic traffic workloads constitute one of the least resource-consuming methods to evaluate the performance. In this work, we propose a synthetic traffic model that emulates the behavior of the Graph500 communications. Our model is empirically obtained through a characterization of several executions of the benchmark with different input parameters. We verify the validity of our model against a characterization of the execution of the benchmark with different parameters. Our model is well-suited for implementation in an architectural simulator. IntroductionBigData applications have become ubiquitous and gather the interest of system architects and designers. The Graph500 benchmark [1] appeared in 2010 with the aim of influencing the design of new systems, so they better adjust to the memory-and IO-bounded requirements of data intensive applications. Based on the execution of a BFS within a graph, it is currently one of the most known BigData-focused benchmarks [3].

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Abstract-Adaptive routing is an efficient congestion avoidance mechanism for modern Datacenter and HPC networks. Congestion detection traditionally relies on the occupancy of the router queues. However, this approach can hinder performance due to coarse-grain measurements with small buffers, and potential routing oscillations with large buffers.We introduce an alternative mechanism, labelled ContentionBased Adaptive Routing. Our mechanism adapts routing based on an estimation of "network contention", the simultaneity of traffic flows contending for a network port. Our system employs a set of counters which track the demand for each output port. This exploits path diversity thanks to earlier detection of adversarial traffic patterns, and decouples buffer size and queue occupancy from contention detection.We evaluate our mechanism in a Dragonfly network. Our evaluations show this mechanism achieves optimal latency under uniform traffic and similar to best previous routing mechanisms under adversarial patterns, with immediate adaptation to traffic pattern changes.