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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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Eliminating voltage emergencies via microarchitectural voltage control feedback and dynamic optimization., and . ISLPED, page 326-331. ACM, (2004)Reducing Exit Stub Memory Consumption in Code Caches., , and . HiPEAC, volume 4367 of Lecture Notes in Computer Science, page 87-101. Springer, (2007)Exploiting Parallelism Opportunities with Deep Learning Frameworks., , , , and . ACM Trans. Archit. Code Optim., 18 (1): 9:1-9:23 (2021)EcoSim: a language and experience teaching parallel programming in elementary school., , , and . SIGCSE, page 51-56. ACM, (2012)Code Cache Management Schemes for Dynamic Optimizers., and . Interaction between Compilers and Computer Architectures, page 102-110. IEEE Computer Society, (2002)Adaptive Online Context-Sensitive Inlining., and . CGO, page 253-264. IEEE Computer Society, (2003)Where is the data? Why you cannot debate CPU vs. GPU performance without the answer., and . ISPASS, page 134-144. IEEE Computer Society, (2011)Bandana: Using Non-volatile Memory for Storing Deep Learning Models., , , , , , , and . CoRR, (2018)Revealing Compiler Heuristics Through Automated Discovery and Optimization., , , , , and . CGO, page 55-66. IEEE, (2024)A Lightweight Algorithm for Dynamic If-Conversion during Dynamic Optimization., and . IEEE PACT, page 71-80. IEEE Computer Society, (2000)