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Deep Learning ▪ Healthcare: Diagnostics and IT Systems Break Tasks Into Artificial Neural Treatment Networks ▪ Supply Chain and Logistics Cognitive, AI and Analytics examples, trends and directions Ulrich Walter Cognitive Systems HPC & Cloud Sales Leader Hanover, 22.12.2017 The world is changing Past Present Processes Future Collecting, Islands Intelligent, Assistants Social Media explosion User genereated Social feedback Collaborative loop buying Mobile Batch oriented revolution Location based Palm sized Digital money And wearable Power of analytics Entire Predictive Integration Manual Processes Analytics Cognitive AI SaaS Cloud enablement IT Centralized System Autonomous Boundary less Living in a smart, intelligent and cognitive world Smart Facilities Intelligent traffic systems Smart Farming Smart Grid Autonomous Driving Intelligent Light System Smart Health Smart Watch Emotionally Intelligent Robots Smart GPS Intelligent Image Recogniton Smart TV Intelligent Response System Smart Phone Intelligent vehicle Smart Refrigerator Intelligent POS and payments Smart Ordnance Smart Camera Intelligent Assistants Smart Surface Intelligent Social Media Analytics Cognitive Fraud Detection Intelligent Museum Cognitive Robotics IBM Systems Two principles of conciousness Cogito, Ergo Sum Γνῶθι σεαυτόν Rene Descartes, 1637 Chilon of Sparta, 555 B.C Overall Artificial Intelligence (AI) Space New class of applications Cognitive / Machine Learing & Training ML/DL ▪ Pattern matching Human Intelligence Exhibited by Machines ▪ Image ▪ Real-time decision support ▪ Complex workflows Machine Learning ▪ Data Lakes New Data “Human Trained” using large amounts of data & ability to learn how to perform the Sources: Extend Enterprise applications task NoSQL, ▪ Finance: Fraud detection / Hadoop & Analytics prevention ▪ Retail: shopping advisors Deep Learning ▪ Healthcare: Diagnostics and IT Systems break tasks into Artificial Neural treatment Networks ▪ Supply chain and logistics Extend Predictive Analytics to Advance Analytics with AI 5 Growing across Compute, Middleware, and Storage Industry examples – Deep Learning Automotive and Security and Public Consumer Web, Medicine and Biology Broadcast, Media and Transportation Safety Mobile, Retail Entertainment • Autonomous driving: • Video Surveillance • Image tagging • Drug discovery • Captioning • Pedestrian detection • Image analysis • Speech recognition • Diagnostic assistance • Search • Accident avoidance • Facial recognition and • Natural language • Cancer cell detection • Recommendations • Maintenance prediction detection • Sentiment analysis • Real time translation IBM Systems Deep Learning in a Nutshell Shallow (supervised) machine learning pipeline Very difficult to find robust mathematical Representations model 0.34 9.34 Feature extraction 1.45 learning 0.01 2.55 Done by human experts “Coffee Mugs” IBM Systems Deep Learning in a Nutshell Deep machinelearning learning pipeline model learning 0.34 9.34 Unstructured Feature extraction 1.45 modeling data 0.01 2.55 Semantic “Coffee Mug” label closed optimization of this problem by Neuronal Networks with many layers IBM Systems Current State of DL Infrastructure Open Source Software ●Caffe (Facebook) ●Torch ●Theano ●Tensor Flow (Google) ●CNTK (Microsoft) ●DSSTNE (Amazon) ●... https://www.bloomberg.com/news/articles/2016-07-21/google-sprints-ahead- in-ai-building-blocks-leaving-rivals-wary IBM Systems By 2022,HPC-driven simulations and deep learning will be the core innovation engines driving 10,000x increase in compute requirements IBM Systems | 10 Some principles of AI Text Semantic Syntax Compress Sentiment Voice Map NLP Consolidated Collect Recommended action Reduce Response or (human intervention) Detect Image & Image recognition Video Analytics Analysis Store Data Sensor Sensor data analytics Automated action POWER AI Framework Data Collection, Storage and Distribution Storage nodes InfiniBand EDR 100GBit Switch Fabric complementing IBM Watson IBM Systems | 11 Sic Transit Gloria Mundi Google Brain 2012 16.000 Servers 3 NVIDIA PASCAL GPUs ~ 8 mW/h ~ 0,9kW/h ~ 50 TFLOPS ~ 62 TFLOPS IBM Systems Power S822LC for HPC (aka Minksy) vs x86 with P100 GPU ▪ 2.8X the CPU-GPU bandwidth compared to x86 based systems – S822LC for HPC with CPU-GPU NVLink capability not available on x86 servers ▪ ~13% faster than any PCI-E platform with 4 GPUs – S822LC for HPC packaging allows for higher power/frequency ▪ X86 P100 PCI-E Performance compares – Kinetica: 2.7X vs x86 with 4 PCI-E based P100 – CPMD: 3X performance of CPU only implementation ▪ The first ever GPU accelerated version of CPMD – NAMD: 30% increase when combine with visualization code IBM Systems 13 POWER 8 CAPI Coherent Accellerator Processor Interface IBM Systems | 14 Compute Node IBM Power 822LC HPC 32 GB 32 GB 32 GB 32 GB 32 GB 32 GB 32 GB 32 GB 32 GB 32 GB 32 GB 32 GB 32 GB 32 GB 32 GB 32 GB 32 GB 32 GB 32 GB 32 GB 32 GB 32 GB 32 GB 32 GB 32 GB 32 GB 32 GB 32 GB 32 GB 32 GB 32 GB 32 GB 4 Lanes / CPU (115GB/s per CPU) POWER8 SMP-A 3 * 12,8GB/s IB EDR Adapter PCI-E3 16GB/s CPU 1 CPU 2 2 * 100 Gbit POWER 8+ POWER 8+ 8 or 10Core 8 or 10Core SSD PEX PEX or NVLINK NVLINK SAS 40GB+40GB 40GB+40GB bidirectional bidirectional On Board NVMe 4 * 10 Gbit Etn 4 * NVIDIA TESLA 100 GPU IBM Systems IBM Systems | 16 Enterprise data sources and analytics Analytics platforms and frameworks People Ecosystems Data + DATA Analytics Intelligence Analytics Things IT Systems Structured & Unstructured data IBM Investment in Innovation Accelerated and Top R&D Machine Learning/ Open Source Data Applications Deep Learning Bases Accelerated DB: Kinetica, Blazegraph PowerAI ML/DL Software Distro (link) GROMACS, Gaussian, NAMD, • Built for Deployment Speed & with Real Performance Optimization VMD, WRF, VASP, • Caffe, Torch, Theano, DIGITS OpenFOAM, LS Dyna, • Python, OpenBLAS and other dependencies AMBER, NCBI – BLAST, GATK4, NWChem GAMESS, Caffe, Torch, Theano, DIGITS, Quantum ESPRESSO TensorFlow, DL4J, more on POWER LAMMPS, CHARMM OSDB: EnterpriseDB, MongoDB, CP2K, LQCD, QMCPack Redis, Neo4J, Cassandra MILC, Chroma, QPACE COSMO, Abinit, COMSOL, CPMD, GTC, HOMME Custom Caffe- CPU/GPU NVLink HYCOM Optimized IBM Systems Built with Collaborative Innovation OpenPOWER Open Source Workloads 299 OpenPOWER members contribute to 87 OpenPOWER Now hyper-focused on expanding Cognitive/AI industry ready products and 17 servers delivering choice to industry applications Close partnership with major AI/accelerator industry leader Enterprise Support/Subscription model Nvidia OpenCAPI Open Frameworks High bandwidth open interconnect to attach to Highly optimized & accelerated Cognitive/AI frameworks accelerators and SCM Cognitive/AI SDK for deployment and deployment tools Several Options to Realize Performance Enhancements via GPU Acceleration Easy Ease of Use Best Application Performance Best Libraries Programing models Programing language which • ESSL/PESSL supporting directives targets GPU • NVIDIA Libraries • OpenACC • CUDA • Math library, cuBlas, • Open MP NPP, etc • Easy to Implement • Modification of existing • Most time intensive • Tested and Supported programs with directives • Requires expertise • Limited – your needs may • Compiler assists with • Achieves best performance not be covered mapping to device results IBM Systems Connecting data islands for a hyperconnected and cognitive digital universe Health & research Security, defence, Weather, climate protection of cyber crime research & Agriculture API API Connected Home Wearables & mobility API Infotainment, industrial & military API health and fitness IBM Hybrid Cloud API IBM IBM Connected, autonomous vehicles Bluemix Watson and intelligent traffic systems API API API API Industrie 4.0 API Energy, utilities and Smart cities Banking, finance & insurance Retail and Marketing IBM Systems Deep learning in action 22 Thank you! ibm.com/systems/hpc IBM Systems | 23.
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