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DTSTART;TZID=America/Chicago:20221117T153000
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UID:submissions.supercomputing.org_SC22_sess253_drs107@linklings.com
SUMMARY:Toward Scalable Middleware for Shared HPC Resources
DESCRIPTION:Doctoral Showcase, Posters\n\nToward Scalable Middleware for S
 hared HPC Resources\n\nRavi\n\nAs computational resources scale larger, ap
 plications often need to be refactored to deal with bottlenecks that arise
  to gain the advantages of strong scaling. When not properly addressed leg
 acy workloads can lead to inefficient usage of available hardware which le
 ads to poor throughput. One solution is to allow multiple tasks to share a
  system to provide multi-tenancy. Multi-tenant environments fall into two 
 categories: time-sharing and space-sharing. Time-sharing has been an effec
 tive technique to deal with multiple applications sharing the CPU and GPU 
 at the node-level. However, time-sharing can have a heavy performance cost
  such as saving and restoring architectural state (context switch overhead
 ) which is very costly on GPUs. While space-sharing can avoid this overhea
 d and improve throughput, current hardware and software systems lack full 
 isolation to provide the necessary quality of service. In this work, we id
 entify key challenges that arise when sharing resources in a HPC context. 
 We evaluate real-world scenarios both at the node-level and cluster-level.
  Using these insights, we propose middleware to mitigate and improve quali
 ty of service.  We introduce a runtime CUDA middleware that improves QoS f
 or GPUs. We also introduce and study two new features of HDF5, GDS VFD and
  Async I/O. The former improves I/O latency while the latter improves and 
 hides variability in I/O latency.\n\nSession Format: Recorded\n\nRegistrat
 ion Category: Tech Program Reg Pass
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