Seastar: a comprehensive framework for telemetry data in HPC environments
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A large number of 2nd generation high-performance computing applications and services rely on adaptive and dynamic architectures and execution strategies to run efficiently,resiliently, and at scale on today’s HPC infrastructures. They require information about applications and their environment to steer and optimize execution. We define this information as telemetry data.Current HPC platforms do not provide the infrastructure,interfaces and conceptual models to collect, store, analyze,and access such data. Today, applications depend on application and platform specific techniques for collecting telemetry data; introducing significant development overheads that inhibit portability and mobility. The development and adoption of adaptive, context-aware strategies is thereby impaired. To facilitate 2nd generation applications,more efficient application development, and swift adoption of adaptive applications in production, a comprehensive framework for telemetry data management must be provided by future HPC systems and services. We introduce Seastar, a conceptual model and a software framework to collect, store, analyze, and exploit streams of telemetry data generated by HPC systems and their applications. We show how Seastar can be integrated with HPC platform architectures and how it enables common application execution strategies.
Weidner , O , Barker , A D & Atkinson , M 2017 , Seastar: a comprehensive framework for telemetry data in HPC environments . in International Workshop on Runtime and Operating Systems for Supercomputers (ROSS 2017) . ACM , International Workshop on Runtime and Operating Systems for Supercomputers , Washington, DC , United States , 27-27 June . DOI: 10.1145/3095770.3095775conference
International Workshop on Runtime and Operating Systems for Supercomputers (ROSS 2017)
© 2017, the Author(s). This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at dl.acm.org / DOI: 10.1145/3095770.3095775
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