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Massive upgrades to science infrastructure are driving data velocitiesupwards while stimulating adoption of increasingly data-intensive analytics.While next-generation exascale supercomputers promise strong support forI/O-intensive workflows, HPC remains largely untapped by live experiments,because data transfers and disparate batch-queueing policies are prohibitivewhen faced with scarce instrument time. To bridge this divide, we introduceBalsam: a distributed orchestration platform enabling workflows at the edge tosecurely and efficiently trigger analytics tasks across a user-managedfederation of HPC execution sites. We describe the architecture of the Balsamservice, which provides a workflow management API, and distributed sites thatprovision resources and schedule scalable, fault-tolerant execution. Wedemonstrate Balsam in efficiently scaling real-time analytics from two DOElight sources simultaneously onto three supercomputers (Theta, Summit, andCori), while maintaining low overheads for on-demand computing, and providing aPython library for seamless integration with existing ecosystems of dataanalysis tools.

Toward Real-time Analysis of Experimental Science Workloads on Geographically Distributed Supercomputers