Unraveling Network-induced Memory Contention: Deeper Insights with Machine Learning

Journal Article · Tue Nov 21 00:00:00 EST 2017 · IEEE Transactions on Parallel and Distributed Systems

DOI:https://doi.org/10.1109/tpds.2017.2773483· OSTI ID:1411596

Groves, Taylor Liles ^[1]; Grant, Ryan ^[2]; Gonzales, Aaron ^[3]; Arnold, Dorian ^[4]

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1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
3. Univ. of New Mexico, Albuquerque, NM (United States)
4. Emory Univ., Atlanta, GA (United States)

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. 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 NiMCs resulting impact on application-level performance. For a range of hardware technologies and HPC workloads, we quantify NiMC and show that NiMCs 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. In addition, 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. Finally, we evaluated three potential techniques to reduce NiMCs impact, namely hardware offloading, core reservation and network throttling.

• Research Organization: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: AC04-94AL85000
• OSTI ID: 1411596
• Report Number(s): SAND-2017-2071J; 651197; TRN: US1800239
• Journal Information: IEEE Transactions on Parallel and Distributed Systems, Vol. 29, Issue 8; ISSN 1045-9219
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
measurement
performance
memory contention
networks
asynchronous communication
machine learning