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Low Power Job Scheduler for Supercomputers: A Rule-Based Power-Aware Scheduler.

, , and . DSDIS, page 732-733. IEEE Computer Society, (2015)

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Combining architectural fault-injection and neutron beam testing approaches toward better understanding of GPU soft-error resilience., , , , and . MWSCAS, page 898-901. IEEE, (2017)MosaiQ: Quantum Generative Adversarial Networks for Image Generation on NISQ Computers., , , , , and . ICCV, page 7007-7016. IEEE, (2023)Examining Failures and Repairs on Supercomputers with Multi-GPU Compute Nodes., , , , and . DSN, page 305-313. IEEE, (2021)Making Disk Failure Predictions SMARTer!, , , , , and . FAST, page 151-167. USENIX Association, (2020)AI-Enabling Workloads on Large-Scale GPU-Accelerated System: Characterization, Opportunities, and Implications., , , , , , , , , and 20 other author(s). HPCA, page 1224-1237. IEEE, (2022)Exploring the Potential of using Power as a First Class Parameter for Resource Allocation in Apache Mesos Managed Clouds., , , and . UCC, page 216-226. IEEE, (2020)Failures in large scale systems: long-term measurement, analysis, and implications., , , and . SC, page 44. ACM, (2017)Job characteristics on large-scale systems: long-term analysis, quantification, and implications., , , , , and . SC, page 84. IEEE/ACM, (2020)Veritas: accurately estimating the correct output on noisy intermediate-scale quantum computers., and . SC, page 15. IEEE/ACM, (2020)Experimental evaluation of NISQ quantum computers: error measurement, characterization, and implications., , , , and . SC, page 46. IEEE/ACM, (2020)