BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:America/Chicago
X-LIC-LOCATION:America/Chicago
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20211207T055409Z
LOCATION:Second Floor Atrium
DTSTART;TZID=America/Chicago:20211116T083000
DTEND;TZID=America/Chicago:20211116T170000
UID:submissions.supercomputing.org_SC21_sess241_spostg113@linklings.com
SUMMARY:cuSZ(+): Optimizing Error-Bounded Lossy Compression for Scientific
  Data on Modern GPUs
DESCRIPTION:ACM Student Research Competition: Graduate Poster, ACM Student
  Research Competition: Undergraduate Poster, Posters\n\ncuSZ(+): Optimizin
 g Error-Bounded Lossy Compression for Scientific Data on Modern GPUs\n\nTi
 an\n\nError-bounded lossy compression is a critical technique for signific
 antly reducing scientific data volumes. With ever-emerging heterogeneous h
 igh-performance computing (HPC) architecture, GPU-accelerated error-bounde
 d compressors (such as cuSZ and cuZFP) have been developed. However, they 
 suffer from either low performance or low compression ratios. To this end,
  we propose cuSZ(+) to target both high compression ratios and throughputs
 . Furthermore, we identify that data smoothness is a vital factor for high
  compression throughputs. Our key contributions are fourfold: (1) We propo
 se an efficient compression workflow to adaptively perform run-length enco
 ding and/or variable-length encoding. (2) We derive Lorenzo reconstruction
  in decompression as multidimensional partial-sum computation and propose 
 its well-formed GPU implementation. (3) We optimize essential kernels and 
 scale to the state-of-the-art A100 GPU. (4) We evaluate cuSZ(+) using real
 -world HPC datasets on V100 and A100. Experiments show cuSZ(+) improves th
 e compression throughputs and ratios by up to 18.4× and 5.3×, respectively
 , over cuSZ.\n\nTag: In-Person Only\n\nRegistration Category: Tech Program
  Reg Pass, Exhibit Hall Only
END:VEVENT
END:VCALENDAR