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Open Access Publications from the University of California

Unraveling network-induced memory contention: Deeper insights with machine learning


Remote Direct Memory Access (RDMA) is expected to be an integral communication mechanism for future exascale systems - enabling asynchronous data transfers, so that applications may fully utilize CPU resources while simultaneously sharing data amongst remote nodes. In this work we examine Network-induced Memory Contention (NiMC) on Infiniband networks. We expose the interactions between RDMA, main-memory and cache, when applications and out-of-band services compete for memory resources. We then explore NiMC's resulting impact on application-level performance. For a range of hardware technologies and HPC workloads, we quantify NiMC and show that NiMC's impact grows with scale resulting in up to 3X performance degradation at scales as small as 8K processes even in applications that previously have been shown to be performance resilient in the presence of noise. Additionally, this work examines the problem of predicting NiMC's impact on applications by leveraging machine learning and easily accessible performance counters. This approach provides additional insights about the root cause of NiMC and facilitates dynamic selection of potential solutions. Lastly, we evaluated three potential techniques to reduce NiMC's impact, namely hardware offloading, core reservation and software-based network throttling.

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