GTC ON-DEMAND

 
SEARCH SESSIONS
SEARCH SESSIONS

Search All
 
Refine Results:
 
Year(s)

SOCIAL MEDIA

EMAIL SUBSCRIPTION

 
 

GTC ON-DEMAND

Presentation
Media
Abstract:

Over the past three decades, the prevalence of dedicated advanced monitoring systems on complex systems has grown extensively. The data aggregated from these monitoring systems, as well as other data sources such as flight test, supply chain, maintenance, safety, operator information, design specs, and logistics, provide valuable insight into how the assets are being operated over their lifespans and enables a wide range of advanced analytics that supports fleet sustainment. Using Sikorsky helicopter fleets as a case study, this talk will discuss how large data sets collected from fleets of assets are used to enhance safety and enable intelligent decision making about the sustainment of the assets including usage monitoring, optimizing maintenance & supply chain, maximizing availability, focusing troubleshooting, and enabling proactive and timely support. This is all enabled by massively scalable data platforms that facilitate the collection, storage, algorithm development, and retrieval of data collected from these fleets of assets. The architecture of this platform and the software and hardware used will be discussed, including the software framework that was created for scaling these resources out to engineering subject matter experts. Specific deep learning applications will also be presented including how deep learning was used for multivariate time series anomaly detection as well as feature extraction and classification.

Over the past three decades, the prevalence of dedicated advanced monitoring systems on complex systems has grown extensively. The data aggregated from these monitoring systems, as well as other data sources such as flight test, supply chain, maintenance, safety, operator information, design specs, and logistics, provide valuable insight into how the assets are being operated over their lifespans and enables a wide range of advanced analytics that supports fleet sustainment. Using Sikorsky helicopter fleets as a case study, this talk will discuss how large data sets collected from fleets of assets are used to enhance safety and enable intelligent decision making about the sustainment of the assets including usage monitoring, optimizing maintenance & supply chain, maximizing availability, focusing troubleshooting, and enabling proactive and timely support. This is all enabled by massively scalable data platforms that facilitate the collection, storage, algorithm development, and retrieval of data collected from these fleets of assets. The architecture of this platform and the software and hardware used will be discussed, including the software framework that was created for scaling these resources out to engineering subject matter experts. Specific deep learning applications will also be presented including how deep learning was used for multivariate time series anomaly detection as well as feature extraction and classification.

  Back
 
Topics:
Virtual Reality & Augmented Reality, Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Washington D.C.
Year:
2018
Session ID:
DC8209
Streaming:
Download:
Share:
 
Abstract:
New algorithms leverage the algebraic strengths of GPUs far beyond rendering visuals. They unlock opportunities for data analysis leveraging computer vision and artificial neural networks. Earlier this year we set out to investigate the deployment of power-efficient GPUs in commodity hardware. We did not focus on supercomputers, but instead exercised GPUs within a homogeneous set of compute nodes like those used to scale Apache Hadoop or Apache Spark clusters. Our work focused on inference deploying models and GPU acceleration for analysis tasks such as feature extraction, identification, and classification not on training or building models, tasks likely better suited to HPC-class machines. Our experiments investigated applications that aren't feasible at scale on existing CPUs, such as malware detection and object detection in images. We'll cover inference on Tesla P4 GPUs in scale-out architectures, leveraging nvidia-docker, Caffe, Torch, and TensorRT.
New algorithms leverage the algebraic strengths of GPUs far beyond rendering visuals. They unlock opportunities for data analysis leveraging computer vision and artificial neural networks. Earlier this year we set out to investigate the deployment of power-efficient GPUs in commodity hardware. We did not focus on supercomputers, but instead exercised GPUs within a homogeneous set of compute nodes like those used to scale Apache Hadoop or Apache Spark clusters. Our work focused on inference deploying models and GPU acceleration for analysis tasks such as feature extraction, identification, and classification not on training or building models, tasks likely better suited to HPC-class machines. Our experiments investigated applications that aren't feasible at scale on existing CPUs, such as malware detection and object detection in images. We'll cover inference on Tesla P4 GPUs in scale-out architectures, leveraging nvidia-docker, Caffe, Torch, and TensorRT.  Back
 
Topics:
Data Center & Cloud Infrastructure, Artificial Intelligence and Deep Learning, Accelerated Data Science
Type:
Talk
Event:
GTC Washington D.C.
Year:
2017
Session ID:
DC7190
Download:
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