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Synthetic Traffic Model of the Graph500 Communications

As Big Data 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 memory-constricted application workloads. These workloads have a significant impact on the memory use and are also communication intensive. A realistic simulation of such benchmarks for architectural research is challenging, since full-system and trace-driven simulators limit severely the size and detail of the workloads that can be used to evaluate the impact of network improvements. Synthetic traffic workloads are one of the least resource-consuming method to evaluate the performance. In this work, we propose a synthetic traffic model that emulates the behavior of the Graph500 communications. This model has been obtained empirically 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.