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:20181221T160742Z
LOCATION:D221
DTSTART;TZID=America/Chicago:20181113T104500
DTEND;TZID=America/Chicago:20181113T110000
UID:submissions.supercomputing.org_SC18_sess277_drs108@linklings.com
SUMMARY:Parallel and Scalable Combinatorial String and Graph Algorithms on
  Distributed Memory Systems
DESCRIPTION:Doctoral Showcase\nComputational Biology, Exascale, GPUs, Grap
 h Algorithms, Linear Algebra, Machine Learning, Sparse Computation, Worksh
 op Reg Pass, Tutorial Reg Pass, Tech Program Reg Pass, Exhibits Reg Pass, 
 Exhibits - Exhibit Hall Only Reg Pass, Doctoral Showcase\n\nParallel and S
 calable Combinatorial String and Graph Algorithms on Distributed Memory Sy
 stems\n\nFlick, Aluru\n\nMethods for processing and analyzing DNA and geno
 mic data are built upon combinatorial graph and string algorithms. The adv
 ent of high-throughput DNA sequencing is enabling the generation of billio
 ns of reads per experiment. Classical and sequential algorithms can no lon
 ger deal with these growing data sizes, which for the last 10 years have g
 reatly out-paced advances in processor speeds. To process and analyze stat
 e-of-the-art genomic data sets require the design of scalable and efficien
 t parallel algorithms and the use of large computing clusters. Here, we pr
 esent our distributed-memory parallel algorithms for indexing large genomi
 c datasets, including algorithms for construction of suffix- and LCP array
 s, solving the All-Nearest-Smaller-Values problem and its application to t
 he construction of suffix trees. Our parallel algorithms exhibit superior 
 runtime complexity and practical performance compared to the state-of-the-
 art. Furthermore, we present distributed-memory algorithms for clustering 
 de-Bruijn graphs and its application to solving a grand challenge metageno
 mic dataset.
URL:https://sc18.supercomputing.org/presentation/?id=drs108&sess=sess277
END:VEVENT
END:VCALENDAR