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Control flow behavior of cloud workloads., , , and . IISWC, page 71-73. IEEE Computer Society, (2014)Increasing GPU Translation Reach by Leveraging Under-Utilized On-Chip Resources., , , and . MICRO, page 1169-1181. ACM, (2021)Lost in Abstraction: Pitfalls of Analyzing GPUs at the Intermediate Language Level., , , , , , , , , and 6 other author(s). HPCA, page 608-619. IEEE Computer Society, (2018)<u>G</u>PU <u>i</u>nitiated <u>O</u>penSHMEM: correct and efficient intra-kernel networking for dGPUs., and . PPoPP, page 336-347. ACM, (2020)Genesys: Automatically generating representative training sets for predictive benchmarking., , , , , , and . SAMOS, page 116-123. IEEE, (2016)Data partitioning strategies for graph workloads on heterogeneous clusters., , , , and . SC, page 56:1-56:12. ACM, (2015)Neighborhood-Aware Address Translation for Irregular GPU Applications., , , and . MICRO, page 352-363. IEEE Computer Society, (2018)Extended task queuing: active messages for heterogeneous systems., , , , , , , , , and 6 other author(s). SC, page 933-944. IEEE Computer Society, (2016)Case Study of Process Variation-Based Domain Partitioning of GPGPUs., , , and . ASYNC, page 119-120. IEEE Computer Society, (2018)Optimizing GPU Cache Policies for MI Workloads., , , , , , , , , and 2 other author(s). IISWC, page 243-248. IEEE, (2019)