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Creators/Authors contains: "Li, Zhuozhao"

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  1. Free, publicly-accessible full text available December 1, 2023
  2. null (Ed.)
    Python has become a widely used programming language for research, not only for small one-off analyses, but also for complex application pipelines running at supercomputer- scale. Modern parallel programming frameworks for Python present users with a more granular unit of management than traditional Unix processes and batch submissions: the Python function. We review the challenges involved in running native Python functions at scale, and present techniques for dynamically determining a minimal set of dependencies and for assembling a lightweight function monitor (LFM) that captures the software environment and manages resources at the granularity of single functions. We evaluate these techniques in a range of environ- ments, from campus cluster to supercomputer, and show that our advanced dependency management planning and dynamic re- source management methods provide superior performance and utilization relative to coarser-grained management approaches, achieving several-fold decrease in execution time for several large Python applications.
  3. Doglioni, C. ; Kim, D. ; Stewart, G.A. ; Silvestris, L. ; Jackson, P. ; Kamleh, W. (Ed.)
    We explore how the function as a service paradigm can be used to address the computing challenges in experimental high-energy physics at CERN. As a case study, we use funcXā€”a high-performance function as a service platform that enables intuitive, flexible, efficient, and scalable remote function execution on existing infrastructureā€”to parallelize an analysis operating on columnar data to aggregate histograms of analysis products of interest in real-time. We demonstrate efficient execution of such analyses on heterogeneous resources.