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DTSTAMP:20260704T220815Z
UID:de376aa7-4070-4974-9b57-d9a944ed4621
DTSTART:20210712T080000Z
DTEND:20210715T140000Z
DESCRIPTION:Attention: On 13 July 2021 we already start at 09:00 CEST!\n\nO
 verview\n\nLearn how to accelerate your applications with OpenACC and CUDA
 \, how to train and deploy a neural network to solve real-world problems\,
  and how to effectively parallelize training of deep neural networks on Mu
 lti-GPUs.\n\nThe online workshop combines lectures about Accelerated Compu
 ting with OpenACC and CUDA with lectures about Fundamentals of Deep Learni
 ng for single and for Multi-GPUs.\n\nThe lectures are interleaved with man
 y hands-on sessions using Jupyter Notebooks. The exercises will be done on
  a fully configured GPU-accelerated workstation in the cloud.\n\nThe works
 hop is co-organized by LRZ and NVIDIA Deep Learning Institute (DLI) for th
 e Partnership for Advanced Computing in Europe (PRACE). LRZ as part of GCS
  is a PRACE Training Centre which serve as European hubs and key drivers o
 f advanced high-quality training for researchers working in the computatio
 nal sciences.\n\nNVIDIA DLI offers hands-on training for developers\, data
  scientists\, and researchers looking to solve challenging problems with d
 eep learning.\n\n All instructors are NVIDIA certified University Ambassa
 dors.\n\nAgenda\n\n1st day: Fundamentals of Accelerated Computing with Ope
 nACC (10:00-16:00 CEST)\n\nOn the first day you learn the basics of OpenAC
 C\, a high-level programming language for programming on GPUs. Discover ho
 w to accelerate the performance of your applications beyond the limits of 
 CPU-only programming with simple pragmas. You’ll learn:\n\n\n	\n	How to 
 profile and optimize your CPU-only applications to identify hot spots for 
 acceleration\n	\n	\n	How to use OpenACC directives to GPU accelerate your 
 codebase\n	\n	\n	How to optimize data movement between the CPU and GPU acc
 elerator\n	\n\n\nUpon completion\, you'll be ready to use OpenACC to GPU a
 ccelerate CPU-only applications.\n\n2nd day: Fundamentals of Accelerated C
 omputing with CUDA C/C++ (09:00-15:00 CEST)\n\nThe CUDA computing platform
  enables the acceleration of CPU-only applications to run on the world’s
  fastest massively parallel GPUs. On the 2nd day you experience C/C++ appl
 ication acceleration by:\n\n\n	\n	Accelerating CPU-only applications to ru
 n their latent parallelism on GPUs\n	\n	\n	Utilizing essential CUDA memory
  management techniques to optimize accelerated applications\n	\n	\n	Exposi
 ng accelerated application potential for concurrency and exploiting it wit
 h CUDA streams\n	\n	\n	Leveraging command line and visual profiling to gui
 de and check your work\n	\n\n\nUpon completion\, you’ll be able to accel
 erate and optimize existing C/C++ CPU-only applications using the most ess
 ential CUDA tools and techniques. You’ll understand an iterative style o
 f CUDA development that will allow you to ship accelerated applications fa
 st.\n\n3rd day: Fundamentals of Deep Learning (10:00-16:00 CEST)\n\nExplor
 e the fundamentals of deep learning by training neural networks and using 
 results to improve performance and capabilities.\n\nDuring this day\, you
 ’ll learn the basics of deep learning by training and deploying neural n
 etworks. You’ll learn how to:\n\n\n	Implement common deep learning workf
 lows\, such as image classification and object detection\n	Experiment with
  data\, training parameters\, network structure\, and other strategies to 
 increase performance and capability\n	Deploy your neural networks to start
  solving real-world problems\n\n\nUpon completion\, you’ll be able to st
 art solving problems on your own with deep learning.\n\n4th day: Fundament
 als of Deep Learning for Multi-GPUs (10:00-16:00 CEST)\n\nThe computationa
 l requirements of deep neural networks used to enable AI applications like
  self-driving cars are enormous. A single training cycle can take weeks on
  a single GPU or even years for larger datasets like those used in self-dr
 iving car research. Using multiple GPUs for deep learning can significantl
 y shorten the time required to train lots of data\, making solving complex
  problems with deep learning feasible.\n\nOn the last day we will teach yo
 u how to use multiple GPUs to train neural networks. You'll learn:\n\n\n	\
 n	Approaches to multi-GPUs training\n	\n	\n	Algorithmic and engineering ch
 allenges to large-scale training\n	\n	\n	Key techniques used to overcome t
 he challenges mentioned above\n	\n\n\nUpon completion\, you'll be able to 
 effectively parallelize training of deep neural networks using TensorFlow.
 \n\nImportant information\n\nAfter you are accepted\, please create an acc
 ount under courses.nvidia.com/join .\n\nEnsure your laptop / PC will run s
 moothly by going to http://websocketstest.com/ Make sure that WebSockets w
 ork for you by seeing under Environment\, WebSockets is supported and Data
  Receive\, Send and Echo Test all check Yes under WebSockets (Port 80).If 
 there are issues with WebSockets\, try updating your browser. If you have 
 any questions\, please contact Marjut Dieringer at mdieringer"at"nvidia.co
 m. \n\nPRACE Training and Education\n\nThe mission of PRACE (Partnership 
 for Advanced Computing in Europe) is to enable high-impact scientific disc
 overy and engineering research and development across all disciplines to e
 nhance European competitiveness for the benefit of society.  PRACE has an
  extensive education and training effort through seasonal schools\, worksh
 ops and scientific and industrial seminars throughout Europe. Seasonal Sch
 ools target broad HPC audiences\, whereas workshops are focused on particu
 lar technologies\, tools or disciplines or research areas.\n\nNVIDIA Deep 
 Learning Institute\n\nThe NVIDIA Deep Learning Institute delivers hands-on
  training for developers\, data scientists\, and engineers. The program is
  designed to help you get started with training\, optimizing\, and deployi
 ng neural networks to solve real-world problems across diverse industries 
 such as self-driving cars\, healthcare\, online services\, and robotics.\n
 \n  \nhttps://events.prace-ri.eu/event/1221/
SUMMARY:[ONLINE] Deep Learning and GPU programming workshop @ LRZ
URL;VALUE=URI:https://events.prace-ri.eu/event/1221/
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