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Creators/Authors contains: "Patel, Tirthak"

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  1. ; ; ; ; ; (, ACM/IEEE)
    Free, publicly-accessible full text available October 25, 2026
  2. ; ; ; ; (, ACM)
    Free, publicly-accessible full text available July 10, 2026
  3. ; ; ; ; (, ACM)
    Free, publicly-accessible full text available July 10, 2026
  4. ; ; (, ASPLOS '22: Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems)
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  5. (, Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems)
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  6. ; ; ; ; (, Social Network Analysis and Mining)
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  7. (, SC20: International Conference for High Performance Computing, Networking, Storage and Analysis)
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  8. ; ; ; ; ; ; (, 2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN))
    GPUs are widely deployed on large-scale HPC systems to provide powerful computational capability for scientific applications from various domains. As those applications are normally long-running, investigating the characteristics of GPU errors becomes imperative for reliability. In this paper, we first study the system conditions that trigger GPU errors using six-month trace data collected from a large-scale, operational HPC system. Then, we use machine learning to predict the occurrence of GPU errors, by taking advantage of temporal and spatial dependencies of the trace data. The resulting machine learning prediction framework is robust and accurate under different workloads. 
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