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Award ID contains: 1753840

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  1. (, PEARC '19: Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning))
    High performance computing systems are typically built with high-throughput and infrastructural uniformity in mind, but generally do not easily accommodate diverse data security requirements on a single cluster. Rather than fracturing that infrastructure by building many network isolated storage "islands" to secure each dataset covered by an individual data use agreement, we explore using the Ceph distributed storage system with client-side encryption to provision secure storage from a single, untrusted data lake. 
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  2. (, SC '19: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis)
    Large-scale applications typically spend a large fraction of their execution time performing I/O to a parallel storage system. However, with rapid progress in compute and storage system stack of large-scale systems, it is critical to investigate and update our understanding of the I/O behavior of large-scale applications. Toward that end, in this work, we monitor, collect and analyze a year worth of storage system data from a large-scale production parallel storage system. We perform temporal, spatial and correlative analysis of the system and uncover surprising patterns which defy existing assumptions and have important implications for future systems. 
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