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Throughput-oriented computing via co-running multiple applications in thesame machine has been widely adopted to achieve high hardware utilization andenergy saving on modern supercomputers and data centers. However, efficientlyco-running applications raises new design challenges, mainly becauseapplications with diverse requirements can stress out shared hardware resources(IO, Network and Cache) at various levels. The disparities in resource usagecan result in interference, which in turn can lead to unpredictable co-runningbehaviors. To better understand application interference, prior work provideddetailed execution characterization. However, these characterization approacheseither emphasize on traditional benchmarks or fall into a single applicationdomain. To address this issue, we study 25 up-to-date applications andbenchmarks from various application domains and form 625 consolidation pairs tothoroughly analyze the execution interference caused by application co-running.Moreover, we leverage mini-benchmarks and real applications to pinpoint theprovenance of co-running interference in both hardware and software aspects.

Characterizing the Performance of Emerging Deep Learning, Graph, and High Performance Computing Workloads Under Interference