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Scalable Data Resilience for In-memory Data Staging., , , , , , and . IPDPS, page 105-115. IEEE Computer Society, (2018)CoREC: Scalable and Resilient In-memory Data Staging for In-situ Workflows., , , , , , and . ACM Trans. Parallel Comput., 7 (2): 12:1-12:29 (2020)Exploring energy and performance behaviors of data-intensive scientific workflows on systems with deep memory hierarchies., , , and . HiPC, page 226-235. IEEE Computer Society, (2013)Practical scalable consensus for pseudo-synchronous distributed systems., , , , , , and . SC, page 31:1-31:12. ACM, (2015)Exploring Automatic, Online Failure Recovery for Scientific Applications at Extreme Scales., , , , , and . SC, page 895-906. IEEE Computer Society, (2014)Exploring Failure Recovery for Stencil-based Applications at Extreme Scales., , , , , , and . HPDC, page 279-282. ACM, (2015)Local recovery and failure masking for stencil-based applications at extreme scales., , , , , , and . SC, page 70:1-70:12. ACM, (2015)Exploring cross-layer power management for PGAS applications on the SCC platform., , , and . HPDC, page 235-246. ACM, (2012)Exploring power behaviors and trade-offs of in-situ data analytics., , , , , , , , , and 3 other author(s). SC, page 77:1-77:12. ACM, (2013)Scalable Failure Masking for Stencil Computations using Ghost Region Expansion and Cell to Rank Remapping., , , , , , and . SIAM J. Sci. Comput., (2017)