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UID:submissions.supercomputing.org_SC24_sess534_drs108@linklings.com
SUMMARY:Enhancing HPC I/O Performance: Leveraging Runtime and Offline I/O 
 Optimization Frameworks
DESCRIPTION:Hammad Bin Ather (University of Oregon)\n\nThe existing parall
 el I/O stack is complex and difficult to tune due to the interdependencies
  among multiple factors that impact the performance of data movement betwe
 en storage and compute systems. When performance is slower than expected, 
 end-users, developers, and system administrators rely on I/O profiling and
  tracing information to pinpoint the root causes of inefficiencies. Despit
 e having numerous tools that collect I/O metrics on production systems, it
  is not obvious where the I/O bottlenecks are (unless one is an I/O expert
 ), their root causes, and what to do to solve them. Hence, there is a gap 
 between the currently available metrics, the issues they represent, and th
 e application of optimizations that would mitigate performance slowdowns. 
 Streamlining such analysis, investigation, and recommendations could close
  this gap without requiring a specialist to intervene in every case. \n\nT
 his dissertation explores how this translation gap can be closed by introd
 ucing two innovative frameworks that leverage both offline and online anal
 ysis and tuning methodologies. The offline framework, named Drishti I/O, p
 rovides interactive visualizations that detail an application's I/O behavi
 or. It pinpoints the root causes of I/O bottlenecks and offers actionable 
 recommendations to enhance performance. The runtime framework extends the 
 capabilities of the Recorder I/O tracing tool by incorporating a dynamic I
 /O prediction and optimization system. This system leverages context-free 
 grammar to optimize I/O behavior in real time during application execution
 . Together, these frameworks offer a comprehensive approach to improving I
 /O performance through detailed analysis and real-time optimizations.\n\nR
 egistration Category: Tech Program Reg Pass, Exhibits Reg Pass\n\nSession 
 Chairs: Ayesha Afzal (Friedrich-Alexander University, Erlangen-Nuremberg; 
 Erlangen National High Performance Computing Center); Sally Ellingson (Uni
 versity of Kentucky); and Alan Sussman (University of Maryland)\n\n
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