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DTSTART;TZID=America/Chicago:20181113T103000
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UID:submissions.supercomputing.org_SC18_sess277@linklings.com
SUMMARY:Doctoral Showcase I
DESCRIPTION:Scalable Methods for Genome Assembly\n\nGenome assembly is a f
 undamental problem in the field of bioinformatics wherein the goal lies in
  the reconstruction of an unknown genome from short DNA fragments obtained
  from it. With the advent of high-throughput sequencing technologies, bill
 ions of reads can be generated in a few hours. My reseac...\n\n\nPriyanka 
 Ghosh and Ananth Kalyanaraman (Washington State University)\n-------------
 --------\nParallel and Scalable Combinatorial String and Graph Algorithms 
 on Distributed Memory Systems\n\nMethods for processing and analyzing DNA 
 and genomic data are built upon combinatorial graph and string algorithms.
  The advent of high-throughput DNA sequencing is enabling the generation o
 f billions of reads per experiment. Classical and sequential algorithms ca
 n no longer deal with these growing d...\n\n\nPatrick Flick and Srinivas A
 luru (Georgia Institute of Technology)\n---------------------\nScalable No
 n-Blocking Krylov Solvers for Extreme-Scale Computing\n\nThis study invest
 igates preconditioned conjugate gradient method variations designed to red
 uce communication costs by decreasing the number of allreduces and overlap
 ping communication with computation using a non-blocking allreduce. Experi
 ments show scalable PCG methods can outperform standard PCG a...\n\n\nPaul
  R. Eller and William Gropp (University of Illinois)\n--------------------
 -\nPattern Matching on Massive Metadata Graphs at Scale\n\nPattern matchin
 g is a powerful graph analysis tool. Unfortunately, existing solutions hav
 e limited scalability, support only a limited set of patterns, and/or focu
 s on only a subset of the real-world problems associated with pattern matc
 hing. First, we present a new algorithmic pipeline based on gra...\n\n\nTa
 hsin Reza and Matei Ripeanu (University of British Columbia)\n------------
 ---------\nIn-Memory Accelerator Architectures for Machine Learning and Bi
 oinformatics\n\nMost contemporary accelerators are von Neumann machines. W
 ith the increasing sizes of gathered and then processed data,  memory band
 width is the main limiting of performance. One approach to mitigate the ba
 ndwidth constraint is to bring the processing units closer to the data. Th
 is approach is ...\n\n\nRoman Kaplan and Ran Ginosar (Israel Institute of 
 Technology)\n---------------------\nLinear Algebra Is the Right Way to Thi
 nk About Graphs\n\nGraph algorithms are challenging to implement on new ac
 celerators such as GPUs. To address this problem, GraphBLAS is an innovati
 ve on-going effort by the graph analytics community to formulate graph alg
 orithms as sparse linear algebra, so that they can be expressed in a perfo
 rmant, succinct and in ...\n\n\nCarl Yang (University of California, Davis
 ; Lawrence Berkeley National Laboratory); John D. Owens (University of Cal
 ifornia, Davis); and Aydin Buluc (Lawrence Berkeley National Laboratory; U
 niversity of California, Berkeley)\n\nTag: Computational Biology, Exascale
 , GPUs, Graph Algorithms, Linear Algebra, Machine Learning, Sparse Computa
 tion, Doctoral Showcase\n\nRegistration Category: Workshop Reg Pass, Tutor
 ial Reg Pass, Tech Program Reg Pass, Exhibits Reg Pass, Exhibits - Exhibit
  Hall Only Reg Pass\n\nSession Chair: Sanjukta Bhowmick (University of Nor
 th Texas)
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