GTC ON-DEMAND

 
SEARCH SESSIONS
SEARCH SESSIONS

Search All
 
Refine Results:
 
Year(s)

SOCIAL MEDIA

EMAIL SUBSCRIPTION

 
 

GTC ON-DEMAND

Presentation
Media
Abstract:
Merlin is a workflow framework that enables orchestrating multi-machine, multi-batch, large-scale science simulation ensembles with in-situ postprocessing, deep learning at-scale using LBANN, surrogate model driven sampling and data exploration. This session describes how MERLIN was used to combine hydrodynamics simulations for ICF with LBANN for training and iterative feedback.
Merlin is a workflow framework that enables orchestrating multi-machine, multi-batch, large-scale science simulation ensembles with in-situ postprocessing, deep learning at-scale using LBANN, surrogate model driven sampling and data exploration. This session describes how MERLIN was used to combine hydrodynamics simulations for ICF with LBANN for training and iterative feedback.  Back
 
Topics:
HPC and Supercomputing
Type:
Talk
Event:
Supercomputing
Year:
2019
Session ID:
SC1908
Streaming:
Share:
 
Abstract:
We propose a new framework for parallelizing deep neural network training that maximizes the amount of data that is ingested by the training algorithm. Our proposed framework called Livermore Tournament Fast Batch Learning (LTFB) targets large-scale data problems. The LTFB approach creates a set of Deep Neural Network (DNN) models and trains each instance of these models independently and in parallel. Periodically, each model selects another model to pair with, exchanges models, and then run a local tournament against held-out tournament datasets. The winning model continues training on the local training datasets. This new approach maximizes computation and minimizes amount of synchronization required in training deep neural network, a major bottleneck in existing synchronous deep learning algorithms. We evaluate our proposed algorithm on two HPC machines at Lawrence Livermore National Laboratory including an early access IBM Power8+ with NVIDIA Tesla P100 GPUs machine. Experimental evaluations of the LTFB framework on two popular image classification benchmark: CIFAR10 and ImageNet, show significant speed up compared to the sequential baseline.
We propose a new framework for parallelizing deep neural network training that maximizes the amount of data that is ingested by the training algorithm. Our proposed framework called Livermore Tournament Fast Batch Learning (LTFB) targets large-scale data problems. The LTFB approach creates a set of Deep Neural Network (DNN) models and trains each instance of these models independently and in parallel. Periodically, each model selects another model to pair with, exchanges models, and then run a local tournament against held-out tournament datasets. The winning model continues training on the local training datasets. This new approach maximizes computation and minimizes amount of synchronization required in training deep neural network, a major bottleneck in existing synchronous deep learning algorithms. We evaluate our proposed algorithm on two HPC machines at Lawrence Livermore National Laboratory including an early access IBM Power8+ with NVIDIA Tesla P100 GPUs machine. Experimental evaluations of the LTFB framework on two popular image classification benchmark: CIFAR10 and ImageNet, show significant speed up compared to the sequential baseline.  Back
 
Topics:
HPC and AI, HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8829
Streaming:
Share:
 
Abstract:
TBA
TBA  Back
 
Topics:
HPC and Supercomputing
Type:
Talk
Event:
SIGGRAPH
Year:
2017
Session ID:
SC1713
Share:
 
Abstract:

In this talk we will describe our recently funded effort to create the CANcer Distributed Learning Environment (CANDLE toolkit) to address one of the challenges identified in the presidential “Precision Medicine Initiative" (PMI). The DOE laboratories in this project are drawing on their strengths in HPC, machine learning and data analytics, and coupling those to the domain strengths of the NCI, particularly in cancer biology and cancer healthcare delivery to bring the full promise of exascale computing to the problem of cancer and precision medicine. This project will focus on three driver cancer problems: RAS protein pathway, drug response, and treatment strategies. We will provide a highlight of these problems, as well as a roadmap for the projects intended research and development efforts.

In this talk we will describe our recently funded effort to create the CANcer Distributed Learning Environment (CANDLE toolkit) to address one of the challenges identified in the presidential “Precision Medicine Initiative" (PMI). The DOE laboratories in this project are drawing on their strengths in HPC, machine learning and data analytics, and coupling those to the domain strengths of the NCI, particularly in cancer biology and cancer healthcare delivery to bring the full promise of exascale computing to the problem of cancer and precision medicine. This project will focus on three driver cancer problems: RAS protein pathway, drug response, and treatment strategies. We will provide a highlight of these problems, as well as a roadmap for the projects intended research and development efforts.

  Back
 
Topics:
HPC and Supercomputing, Artificial Intelligence and Deep Learning
Type:
Talk
Event:
Supercomputing
Year:
2016
Session ID:
SC6124
Streaming:
Share:
 
 
 
Previous
  • Amazon Web Services
  • IBM
  • Cisco
  • Dell EMC
  • Hewlett Packard Enterprise
  • Inspur
  • Lenovo
  • SenseTime
  • Supermicro Computers
  • Synnex
  • Autodesk
  • HP
  • Linear Technology
  • MSI Computer Corp.
  • OPTIS
  • PNY
  • SK Hynix
  • vmware
  • Abaco Systems
  • Acceleware Ltd.
  • ASUSTeK COMPUTER INC
  • Cray Inc.
  • Exxact Corporation
  • Flanders - Belgium
  • Google Cloud
  • HTC VIVE
  • Liqid
  • MapD
  • Penguin Computing
  • SAP
  • Sugon
  • Twitter
Next