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DTSTAMP:20210402T160548Z
LOCATION:Track 3
DTSTART;TZID=America/New_York:20201118T130000
DTEND;TZID=America/New_York:20201118T143000
UID:submissions.supercomputing.org_SC20_sess158@linklings.com
SUMMARY:GPU Algorithms and Optimizations
DESCRIPTION:Paper\n\nNewton-ADMM: A Distributed GPU-Accelerated Optimizer 
 for Multiclass Classification Problems\n\nFang, Kylasa, Roosta, Mahoney, G
 rama\n\nFirst-order optimization techniques, such as stochastic gradient d
 escent (SGD) and its variants, are widely used in machine learning applica
 tions due to their simplicity and low per-iteration costs. They often requ
 ire, however, large numbers of iterations, with associated communication c
 osts in dist...\n\n---------------------\nPREEMPT: Scalable Epidemic Inter
 ventions Using Submodular Optimization on Multi-GPU Systems\n\nMinutoli, S
 ambaturu, Halappanavar, Tumeo, Kalyanaraman...\n\nPreventing and slowing t
 he spread of epidemics are achieved through techniques such as vaccination
  and social distancing. Given practical limitations on the number of vacci
 nes and the cost of administration, optimization becomes a necessity. Prev
 ious approaches using mathematical programming methods...\n\n-------------
 --------\nC-SAW: A Framework for Graph Sampling and Random Walk on GPUs\n\
 nPandey, Li, Hoisie, Li, Liu\n\nMany applications have requirements to ana
 lyze, transform, visualize and learn large scale graphs. These graphs are 
 often too large to be addressed efficiently using conventional graph proce
 ssing technologies. Recent literatures convey that graph sampling/random w
 alk could be an efficient solution. ...\n\n\nTag: Accelerators, FPGA, and 
 GPUs, Algorithms, Graph Algorithms, Scalable Computing\n\nRegistration Cat
 egory: Tech Program Reg Pass
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