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Increasing GPU Translation Reach by Leveraging Under-Utilized On-Chip Resources.

, , , and . MICRO, page 1169-1181. ACM, (2021)

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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)<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)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)GPGPU Benchmark Suites: How Well Do They Sample the Performance Spectrum?, , , , , and . ICPP, page 320-329. IEEE Computer Society, (2015)Watt Watcher: Fine-Grained Power Estimation for Emerging Workloads., , , and . SBAC-PAD, page 106-113. IEEE Computer Society, (2015)Performance Characterization of Modern Databases on Out-of-Order CPUs., , , , and . SBAC-PAD, page 114-121. IEEE Computer Society, (2015)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)