BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:America/New_York
X-LIC-LOCATION:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20260422T143139Z
LOCATION:B304
DTSTART;TZID=America/New_York:20241118T105500
DTEND;TZID=America/New_York:20241118T111000
UID:submissions.supercomputing.org_SC24_sess749_ws_drbsd108@linklings.com
SUMMARY:BCSR on GPU: A Way Forward Extreme-scale Graph Processing on Accel
 erator-enabled Frontier Supercomputer
DESCRIPTION:Naw Safrin Sattar, Hao Lu, and Feiyi Wang (Oak Ridge National 
 Laboratory (ORNL))\n\nHandling large graphs in a distributed environment r
 equires effective partitioning across processors and efficient management 
 of local partitions. In 2D partitioning, local graphs often become too spa
 rse, making memory-efficient data structures crucial. Using the Compressed
  Sparse Row (CSR) format wastes space, especially for >83% of vertices wit
 h empty edges for the sparse graphs. This study explores bit-CSR (BCSR), a
  modified CSR representation, on GPUs to reduce memory usage in graph comp
 utations. We achieved 16.67\% memory savings on a sparse rmat dataset with
  268 million vertices and 357 million edges, without performance degradati
 on, supported by both theoretical and experimental storage savings of 33%.
  However, we observed a 1.7x slowdown in degree lookup times due to bitwis
 e operations on AMD CPUs. This analysis highlights the potential of BCSR o
 n GPUs for improving Graph500 benchmark performance on GPU-accelerated sys
 tems, such as the Frontier supercomputer.\n\nTag: Data Compression, Data M
 ovement and Memory, Middleware and System Software\n\nRegistration Categor
 y: Workshop Reg Pass\n\nSession Chairs: Sheng Di (Argonne National Laborat
 ory (ANL), University of Chicago); Ana Gainaru (Oak Ridge National Laborat
 ory (ORNL)); Sian Jin (Temple University); Xin Liang (Oregon State Univers
 ity); Kento Sato (RIKEN Center for Computational Science (R-CCS)); and Din
 gwen Tao (Institute of Computing Technology, Chinese Academy of Sciences; 
 University of Chinese Academy of Sciences)\n\n
END:VEVENT
END:VCALENDAR