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DTSTART:19700308T020000
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DTSTAMP:20210402T160548Z
LOCATION:Track 3
DTSTART;TZID=America/New_York:20201112T100000
DTEND;TZID=America/New_York:20201112T172000
UID:submissions.supercomputing.org_SC20_sess209@linklings.com
SUMMARY:Machine Learning in HPC Environments
DESCRIPTION:Workshop\n\nA Benders Decomposition Approach to Correlation Cl
 ustering\n\nLukasik, Keuper, Singh, Yarkony\n\nWe tackle the problem of gr
 aph partitioning for image segmentation using correlation clustering (CC),
  which we treat as an integer linear program (ILP). We reformulate optimiz
 ation in the ILP so as to admit efficient optimization via Benders decompo
 sition, a classic technique from operations researc...\n\n----------------
 -----\nIntroduction: Machine Learning in HPC Environments\n\nLim, Shen, Ke
 uper, Houston\n\nThe intent of this workshop is to bring together research
 ers, practitioners and scientific communities to discuss methods that util
 ize extreme scale systems for machine learning. This workshop will focus o
 n the greatest challenges in utilizing HPC for machine learning and method
 s for exploiting data...\n\n---------------------\nMachine Learning in HPC
  Environments – Lunch Break\n\n\n\n---------------------\nDeep Generative 
 Models that Solve PDEs: Distributed Computing for Training Large Data-Free
  Models\n\nBotelho, Joshi, Khara, Rao, Sarkar...\n\nRecent progress in sci
 entific machine learning (SciML) has opened up the possibility of training
  novel neural network architectures that solve complex partial differentia
 l equations (PDEs). Several (nearly data free) approaches have been recent
 ly reported that successfully solve PDEs, with examples ...\n\n-----------
 ----------\nHigh-Bypass Learning: Automated Detection of Tumor Cells that 
 Significantly Impact Drug Response\n\nWozniak, Yoo, Mohd-Yusof, Nicolae, T
 urgeon...\n\nMachine learning in biomedicine is reliant on the availabilit
 y of large, high-quality data sets.  These corpora are used for training s
 tatistical or deep learning -based models that can be validated against ot
 her data sets and ultimately used to guide decisions.  The quality of thes
 e data sets is an...\n\n---------------------\nAccelerate Distributed Stoc
 hastic Gradient Descent for Nonconvex Optimization with Momentum\n\nCong, 
 liu\n\nMomentum method has been used extensively in optimizers for deep le
 arning. Recent studies show that distributed training through K-step avera
 ging has many nice properties. We propose a momentum method for such model
  averaging approaches. At each individual learner level traditional stocha
 stic gradie...\n\n---------------------\nEventGraD: Event-Triggered Commun
 ication in Parallel Stochastic Gradient Descent\n\nGhosh, Gupta\n\nCommuni
 cation in parallel systems consumes significant amount of time and energy 
 which often turns out to be a bottleneck in distributed machine learning. 
 In this paper, we present EventGraD - an algorithm with event-triggered co
 mmunication in parallel stochastic gradient descent. The main idea of t...
 \n\n---------------------\nMachine Learning in HPC Environments – Concludi
 ng Remarks\n\n\n\n---------------------\nFairness, Accountability, Transpa
 rency, and Ethics in Computer Vision\n\nGebru\n\n---------------------\nAc
 celerating GPU-Based Machine Learning in Python Using MPI Library: A Case 
 Study with MVAPICH2-GDR\n\nGhazimirsaeed, Anthony, Shafi, Subramoni, Panda
 \n\nThe growth of big data applications during the last decade has led to 
 a surge in the deployment and popularity of machine learning (ML) librarie
 s. On the other hand, the high performance offered by GPUs makes them well
  suited for ML problems. To take advantage of GPU performance for ML, NVID
 IA has r...\n\n---------------------\nMachine Learning in HPC Environments
  – Break\n\n\n\n---------------------\nKeynote: Michael Garland - Programm
 ing Systems of Data\n\nGarland\n\nMachine learning and data analysis thriv
 e on mass quantities of data.  At the same time, the cost of data distribu
 tion and movement is among the most critical factors determining the perfo
 rmance of applications at scale.  Consequently, scalable high-performance 
 machine learning and data analysis req...\n\n---------------------\nMachin
 e Learning in HPC Environments – Break\n\n\n\n\nRegistration Category: Wor
 kshop Reg Pass
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