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Understanding Training Efficiency of Deep Learning Recommendation Models at Scale.

, , , , , and . HPCA, page 802-814. IEEE, (2021)

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MAD Max Beyond Single-Node: Enabling Large Machine Learning Model Acceleration on Distributed Systems., , , , , , , and . CoRR, (2023)Understanding Training Efficiency of Deep Learning Recommendation Models at Scale., , , , , and . HPCA, page 802-814. IEEE, (2021)Mitigating Processor Variation through Dynamic Load Balancing., and . IPDPS Workshops, page 1073-1076. IEEE Computer Society, (2016)UIUC-PPL/charm: Charm++ version 6.10.2., , , , , , , , , and 68 other author(s). (August 2020)Power Aware Heterogeneous Node Assembly., , , and . HPCA, page 715-727. IEEE, (2019)Mitigating variability in HPC systems and applications for performance and power efficiency. University of Illinois Urbana-Champaign, USA, (2017)Scalable molecular dynamics with NAMD on the Summit system., , , , , and . IBM J. Res. Dev., 62 (6): 4:1-4:9 (2018)Thermal aware automated load balancing for HPC applications., , , , and . CLUSTER, page 1-8. IEEE Computer Society, (2013)Towards realizing the potential of malleable jobs., , , and . HiPC, page 1-10. IEEE Computer Society, (2014)UIUC-PPL/charm: v6.10.1., , , , , , , , , and 68 other author(s). (March 2020)