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DTSTART;TZID=America/Chicago:20181113T103000
DTEND;TZID=America/Chicago:20181113T120000
UID:submissions.supercomputing.org_SC18_sess277@linklings.com
SUMMARY:Doctoral Showcase I
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\nLinear Algebra
  Is the Right Way to Think About Graphs\n\nYang, Owens, Buluc\n\nGraph alg
 orithms are challenging to implement on new accelerators such as GPUs. To 
 address this problem, GraphBLAS is an innovative on-going effort by the gr
 aph analytics community to formulate graph algorithms as sparse linear alg
 ebra, so that they can be expressed in a performant, succinct and in ...\n
 \n---------------------\nParallel and Scalable Combinatorial String and Gr
 aph Algorithms on Distributed Memory Systems\n\nFlick, Aluru\n\nMethods fo
 r processing and analyzing DNA and genomic data are built upon combinatori
 al graph and string algorithms. The advent of high-throughput DNA sequenci
 ng is enabling the generation of billions of reads per experiment. Classic
 al and sequential algorithms can no longer deal with these growing d...\n\
 n---------------------\nScalable Non-Blocking Krylov Solvers for Extreme-S
 cale Computing\n\nEller, Gropp\n\nThis study investigates preconditioned c
 onjugate gradient method variations designed to reduce communication costs
  by decreasing the number of allreduces and overlapping communication with
  computation using a non-blocking allreduce. Experiments show scalable PCG
  methods can outperform standard PCG a...\n\n---------------------\nIn-Mem
 ory Accelerator Architectures for Machine Learning and Bioinformatics\n\nK
 aplan, Ginosar\n\nMost contemporary accelerators are von Neumann machines.
  With the increasing sizes of gathered and then processed data,  memo
 ry bandwidth is the main limiting of performance. One approach to mitigate
  the bandwidth constraint is to bring the processing units closer to the d
 ata. This approach is ...\n\n---------------------\nPattern Matching on Ma
 ssive Metadata Graphs at Scale\n\nReza, Ripeanu\n\nPattern matching is a p
 owerful graph analysis tool. Unfortunately, existing solutions have limite
 d scalability, support only a limited set of patterns, and/or focus on onl
 y a subset of the real-world problems associated with pattern matching. Fi
 rst, we present a new algorithmic pipeline based on gra...\n\n------------
 ---------\nScalable Methods for Genome Assembly\n\nGhosh, Kalyanaraman\n\n
 Genome assembly is a fundamental problem in the field of bioinformatics wh
 erein the goal lies in the reconstruction of an unknown genome from short 
 DNA fragments obtained from it. With the advent of high-throughput sequenc
 ing technologies, billions of reads can be generated in a few hours. My re
 seac...\n
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