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BEGIN:VEVENT
DTSTAMP:20181221T160906Z
LOCATION:D167/174
DTSTART;TZID=America/Chicago:20181112T090000
DTEND;TZID=America/Chicago:20181112T173000
UID:submissions.supercomputing.org_SC18_sess151@linklings.com
SUMMARY:Machine Learning in HPC Environments
DESCRIPTION:Workshop\nDeep Learning, Machine Learning, Workshop Reg Pass\n
 \nCommunication-Efficient Parallelization Strategy for Deep Convolutional 
 Neural Network Training\n\nLee, Agrawal, Balaprakash, Choudhary, Liao\n\nT
 raining modern Convolutional Neural Network (CNN) models is extremely time
 -consuming, and the efficiency of its parallelization plays a key role in 
 finishing the training in a reasonable amount of time. The well-known para
 llel synchronous Stochastic Gradient Descent (SGD) algorithm suffers from 
 hig...\n\n---------------------\nIntroduction - Machine Learning in HPC En
 vironments\n\nYoung, Patton, Keuper, Houston\n\nThe intent of this worksho
 p is to bring together researchers, practitioners, and scientific communit
 ies to discuss methods that utilize extreme scale systems for machine lear
 ning. This workshop will focus on the greatest challenges in utilizing HPC
  for machine learning and methods for exploiting dat...\n\n---------------
 ------\nLarge-Scale Clustering Using MPI-Based Canopy\n\nHeinis\n\nAnalyzi
 ng massive amounts of data and extracting value from it has become key acr
 oss different disciplines. Many approaches have been developed to extract 
 insight from the plethora of data available.  As the amount of data grow r
 apidly, however, current approaches for analysis struggle to scale. Thi...
 \n\n---------------------\nWorkshop Lunch (on your own)\n\n\n\n-----------
 ----------\nAutomated Labeling of Electron Microscopy Images Using Deep Le
 arning\n\nWeber, Ophus, Ramakrishnan\n\nSearching for scientific data requ
 ires metadata providing a relevant context. Today, generating metadata is 
 a time and labor intensive manual process that is often neglected, and imp
 ortant datasets are not accessible through search. We investigate the use 
 of machine learning to generalize metadata f...\n\n---------------------\n
 Scaling Deep Learning for Cancer with Advanced Workflow Storage Integratio
 n\n\nWozniak, Davis, Shu, Ozik, Collier...\n\nCancer Deep Learning Environ
 ment (CANDLE) benchmarks and workflows will combine the power of exascale 
 computing with neural network-based machine learning to address a range of
  loosely connected problems in cancer research.  This application area pos
 es unique challenges to the exascale computing env...\n\n-----------------
 ----\nOn Adam-Trained Models and a Parallel Method to Improve the Generali
 zation Performance\n\nCong, Buratti\n\nAdam is a popular stochastic optimi
 zer that uses adaptive estimates of lower-order moments to update weights 
 and requires little hyper-parameter tuning. Some recent studies have calle
 d the generalization and out-of-sample behavior of such adaptive gradient 
 methods into question, and argued that such...\n\n---------------------\nA
 fternoon Keynote - Robinson Pino (DOE ASCR)\n\nPino\n\n-------------------
 --\nMorning Keynote – Azalia Mirhoseini (Google)\n\nMirhoseini\n\nAdvances
  in computer systems have been key to the success of Machine Learning (ML)
  in recent years. With the ubiquitous success of ML, it is now time for a 
 new era where we can transform the way computer systems are built -- with 
 learning. This talk highlights some of the challenges that modern comp...\
 n\n---------------------\nAluminum: An Asynchronous, GPU-Aware Communicati
 on Library Optimized for Large-Scale Training of Deep Neural Networks on H
 PC Systems\n\nDryden, Maruyama, Moon, Benson, Yoo...\n\nWe identify commun
 ication as a major bottleneck for training deep neural networks on large-s
 cale GPU clusters, taking over 10x as long as computation. To reduce this 
 overhead, we discuss techniques to overlap communication and computation a
 s much as possible. This leads to much of the communication ...\n\n-------
 --------------\nWorkshop Morning Break\n\n\n\n---------------------\nOptim
 izing Machine Learning on Apache Spark in HPC Environments\n\nLi, Davis, J
 arvis\n\nMachine learning has established itself as a powerful tool for th
 e construction of decision making models and algorithms through the use of
  statistical techniques on training data. However, a significant impedimen
 t to its progress is the time spent training and improving the accuracy of
  these models...\n\n---------------------\nLarge Minibatch Training on Sup
 ercomputers with Improved Accuracy and Reduced Time to Train\n\nCodreanu, 
 Podareanu, Saletore\n\nFor the past 6 years, the ILSVRC competition and th
 e ImageNet dataset have attracted a lot of interest from the Computer Visi
 on community, allowing for state-of-the-art accuracy to grow tremendously.
  This should be credited to the use of deep artificial neural network desi
 gns. As these became more c...\n\n---------------------\nWorkshop Overview
 \n\n\n\n---------------------\nWorkshop Overview\n\n\n\n------------------
 ---\nWorkshop Afternoon Break\n\n\n
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