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AI OpenPOWER Academia Discussion Group

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OpenPOWER AI Academia Discussion Group

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AI OpenPOWER Academia Discussion Group

  1. 1. OpenPOWER and AI Workshop Ganesan Narayanasamy IBM
  2. 2. Welcome you all for the AI and OpenPOWER Bootcamp 6/2 0/2 2
  3. 3. OpenPOWER & AI Workshop at BSC ,Barcelona By OpenPOWER Academia Day 1 is meant as an introduction for everyone interested in using AI. Day 2 is meant to go deeper with those who have especially challenging projects. on 18th and 19th June 2018
  4. 4. Agenda Day 1 - June 18th 2018 9:00 a.m to 9.30 a.m. 9.30 a.m to 10.15 am 10.15 am to 10.30 am 10.30 am to 11.15 am 11.15 am to 12.00 Noon 12.00 Noon to 1.00 pm Welcome and OpenPOWER ADG features Introduction to Power 9 and PowerAI Break Large Model Support and Distributed Deep Learning Use Case Demonstration with PowerAI Lunch 1.00pm to 1.45 pm 1.45 pm to 2.45 pm 2.45 pm to 3.00 pm 3.00.pm to 3.45pm 3.45 pm to 4.45 pm 4.45 pm to 5.00 pm Mellanox Feature Updates CFD Simulation on Power Break Introduction to Snap Machine Learning Snap Machine Learning Demos , Q&A Wrap up and Q & A
  5. 5. Agenda Day 2 - June 19th 2018 9.00 am to 9.30 am 9.30 am to 12.00 pm 12.00 pm to 1.00 pm 01.00 pm to 04.30 pm Quick review about Day I Deep Learning Exercise II using Nimbix /Other Infra Industry specific use cases ( LMS ) Lunch Deep Learning Exercise II using Nimbix/Other infra Industry specific Use cases using P9 features ( LMS and DDL )
  6. 6. Founding Members in 2013
  7. 7. Ecosystem
  8. 8. Chip / SOC This is What A Revolution Looks Like © 2018 OpenPOWER Foundation I/O / Storage / Acceleration Boards / Systems Software System / Integration Implementation / HPC / Research
  9. 9. Software Boards / Systems System / Integration I/O / Storage / Acceleration Implementation / HPC / Research Chip / SOC This is What A Revolution Looks Like © 2017 OpenPOWER Foundation 328+ Members 33 Countri es 70+ ISVs
  10. 10. Chip / SOC This is What A Revolution Looks Like © 2017 OpenPOWER Foundation I/O / Storage / Acceleration Implementation / HPC / Research Boards / Systems System / Integration Software 328+ Members 33 Countri es 70+ ISVs Active Membership From All Layers of the Stack 100k+ Linux Applications Running on Power 2300 ISVs Written Code on Linux Partners Bring Systems to Market 150+ OpenPOWER Ready Certified Products 20+ Systems Manufacturers 40+ POWER-based systems shipping or in development 100+ Collaborative innovations under way
  11. 11. OpenPOWER in Action 6/2 0/2 12
  12. 12. What is CORAL? The program through which Summit & Sierra are procured.  Several DOE labs have strong supercomputing programs and facilities.  To bring the next generation of leading supercomputers to these labs, DOE created CORAL (the Collaboration of Oak Ridge, Argonne, and Livermore) to jointly procure these systems, and in so doing, align strategy and resources across the DOE enterprise.  Collaboration grouping of DOE labs was done based on common acquisition timings. Collaboration is a win-win for all parties. “Summit” System “Sierra” System OpenPOWER Technologies: IBM POWER CPUs, NVIDIA Tesla GPUs, Mellanox EDR 100Gb/s InfiniBand Paving The Road to Exascale Performance
  13. 13. Academic Membership  Currently about 100+ academic members in OPF 14 A*STAR ASU ASTRI Moscow State University Carnegie Mellon Univ. CDAC Colorado School of Mines CINECA CFMS Coimbatore Institute of Technology Dalian University of Technology GSIC Hartree Centre ICM IIIT Bangalore IIT Bombay Indian Institute for Technology Roorkee ICCS INAF FZ Jülich LSU BSC Nanyang Technological University National University of Singapore NIT Mangalore NIT Warangal Northeastern University in China ORNL OSU RICE Rome HPC Center LLNL SANDIA SASTRA University Seoul National University Shanghai Shao Tong University SICSR TEES Tohoku University Tsinghua University University of Arkansas SDSC Unicamp University of Central Florida University of Florida University of Hawai University of Hyderabad University of Illinois University of Michigan University of Oregon University of Patras University of Southern California TACC Waseda University IISc ,Loyola,IIT Roorkee
  14. 14. Goals of the Academia Discussion Group  Provide training and exchange of experience and know-how  Provide platform for networking among academic members  Work on engagement of HPC community  Enable co-design/development activities 15 6/2 0/2
  15. 15. Conclusions  Growing number of academic organizations have become member of the OpenPOWER Foundation  The Academia Discussion Groups provides a platform for training, networking, engagement and enablement of co-design  Those who have not yet joined: You are welcome to join https://members.openpowerfoundation.org/wg/AcademiaDG/mail/index  OpenPOWER AI virtual University's focus on bringing together industry, government and academic expertise to connect and help shape the AI future .  https://www.youtube.com/channel/UCYLtbUp0AH0ZAv5mNut1Kcg 16 6/2 0/2
  16. 16. Power 9 Advantages ( AC922)
  17. 17. 1. CPU - POWER9 NZ gzip, has a potential when working with compressed-full workload to reduced memory foot print and I/O bottlenecks in pre-processing stage; is not today available but hopefully we will get this soon; - CPU has direct access to GPU memory without need for migration; not explored today in TF or Caffe part of PowerAI - VSX3 can accelerate the media processing/pre-processing for computer vision http://www.eecg.utoronto.ca/~moshovos/ACA06/readings/altivec.pdf 2. System’s Memory - 8x DDR4 memory channels will always give more performance and prevent memory contention in AI workloads - Managed memory is cache-coherent between CPU & GPU; not explored today in TF or Caffe part of PowerAI
  18. 18. 3. GPU - NVLINK 2.0 with the CPU allows faster data movement from the CPU to the GPU when datasets are larger in range of TB's - GPUDirect RDMA to unified memory; don't think is explored today in TF or Caffe part of PowerAI - technology such LMS are best feet for large models like deep residual networks / ResNet-152 https://arxiv.org/pdf/1803.06333 4. InfiniBand - MPI / DDL / Horovod have the potential to explore this unique multi-host socket direct adapter and provide lowest possible latency between many learners when training. This will lead to lower training times. Posible improvements in training efficiency over exiting research paper: https://arxiv.org/pdf/1708.02188
  19. 19. 5. I/O: - PCIe Gen4 offers for NVMe adapters more bandwidth used for caching datasets into compute nodes more closer to the GPUs (13.5GB/s vs 6.8GB/s in PCIe Gen3); this is helping very much in pre-fetching the data into the system memory - OpenCAPI provides more bandwidth for other type of accelerators such FPGA's give then option of fast inference processes; possible other kinds of DRAM in the feature. 6. Others: - Water cooled systems available for 4x GPUs and 6x GPUs are making the AI solutions much more efficient at scale taken into consideration 300W/GPU power consumption.
  20. 20. THANK YOU!

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