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UID:submissions.supercomputing.org_SC24_sess533_post177@linklings.com
SUMMARY:AI-Based Scalable Analytics for Improving Performance and Resilien
 ce of HPC Systems
DESCRIPTION:Efe Sencan, Beste Oztop, Ayse Coskun, Brian Kulis, and Manuel 
 Egele (Boston University) and Benjamin Schwaller, Vitus Leung, and Jim Bra
 ndt (Sandia National Laboratories)\n\nAs high-performance computing (HPC) 
 advances to exascale levels, its role in scientific fields such as medicin
 e, climate research, finance, and scientific computing becomes increasingl
 y critical. However, these large-scale systems are susceptible to performa
 nce variations caused by anomalies, including network contention, hardware
  malfunctions, and shared resource conflicts. These anomalies can lead to 
 increased energy consumption, scheduling inefficiencies, and reduced appli
 cation performance. Therefore, accurately and promptly diagnosing these pe
 rformance anomalies is essential for maintaining the efficiency and reliab
 ility of HPC systems. Machine learning offers a powerful approach to autom
 ating the detection of such anomalies by learning patterns from the vast a
 mounts of complex telemetry data generated by these systems. Our research 
 focuses on increasing the efficiency and resilience of HPC systems through
  automated telemetry analytics, and this poster presentation will summariz
 e our efforts and findings in this domain.\n\nRegistration Category: Tech 
 Program Reg Pass, Exhibits Reg Pass\n\nSession Chairs: Ayesha Afzal (Fried
 rich-Alexander University, Erlangen-Nuremberg; Erlangen National High Perf
 ormance Computing Center); Sally Ellingson (University of Kentucky); and A
 lan Sussman (University of Maryland)\n\n
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