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