Aplicações Potenciais de Deep Learning à Indústria do Petróleo
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The document discusses the advancements and applications of GPU technology, particularly in high-performance computing (HPC), deep learning, and big data analytics, highlighted during the GPU Technology Conference held in Silicon Valley from May 8 to 11, 2017. It covers the evolution of microprocessors and GPU computing performance, NVIDIA's contributions to supercomputers in the oil and gas industry, and the growth of deep learning as a transformative technology. Furthermore, it introduces NVIDIA's software ecosystem and hardware capabilities for accelerating deep learning and analytics, showcasing their significance across various industries.
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RESERVOIR SIMULATION
Company Simulator/Method Model
Production
Simulation
Runtime Reference Cores/Servers
Saudi Aramco GIGAPOWERS
Three-phase black oil
1.03 Billion cells
3,000 wells
60 years 4 days
[1]
Saudi Aramco GIGAPOWERS
Three-phase black oil
1.03 Billion cells
3,000 wells
60 years 21 hours
[2]
5640 Cores
470 Servers
Total/Schlumberger INTERSECT 1.1 Billion cells
361 wells
20 years 10.5 hours
[3]
576 Cores
288 Servers
ExxonMobil
?
1 Billion cells
? ? ?
716,800 Cores
22,400 Servers
StoneRidge Echelon
Three-phase black oil
1.01 Billion cells
1,000 wells
45 years 92 minutes
?
120 GPUS
30 Servers
Performance Comparison
[1] SPE 119272 “A Next-Generation Parallel Reservoir Simulator for Giant Reservoirs”, A. Dogru et. al. 2009 SPE Reservoir Simulation Symposium.
[2] SPE 142297 “New Frontiers in Large Scale Reservoir Simulation”, A. Dogru et. al. 2011 SPE Reservoir Simulation Symposium.
[3] IPTC 17648 “Giga Cell Compositional Simulation”, E. Obi et. al., 2014 International Petroleum Technology Conference.](https://image.slidesharecdn.com/nvidia-pedrosemanadeinvernodegeofisica-2017-170720192843/85/Aplicacoes-Potenciais-de-Deep-Learning-a-Industria-do-Petroleo-14-320.jpg)
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NEW TENSOR CORE
New CUDA TensorOp instructions
& data formats
4x4 matrix processing array
D[FP32] = A[FP16] * B[FP16] + C[FP32]
Optimized for deep learning
Activation Inputs Weights Inputs Output Results](https://image.slidesharecdn.com/nvidia-pedrosemanadeinvernodegeofisica-2017-170720192843/85/Aplicacoes-Potenciais-de-Deep-Learning-a-Industria-do-Petroleo-58-320.jpg)
Aplicações Potenciais de Deep Learning à Indústria do Petróleo
- 1. Pedro Mario Cruz e Silva ([email protected]) Solution Architect Enterprise Latin America Global O&G Team HPC, DEEP LEARNING, AND BIG DATA & ANALYTICS
- 2. 2 May 8 - 11, 2017 | Silicon Valley | #GTC17 www.gputechconf.com CONNECT Connect with technology experts from NVIDIA and other leading organizations LEARN Gain insight and valuable hands-on training through hundreds of sessions and research posters DISCOVER See how GPUs are creating amazing breakthroughs in important fields such as deep learning and AI INNOVATE Hear about disruptive innovations from startups The world’s most important event for GPU developers May 8 – 11, 2017 in Silicon Valley http://on-demand-gtc.gputechconf.com
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- 4. 4 LIFE AFTER MOORE’S LAW 1980 1990 2000 2010 2020 102 103 104 105 106 107 40 Years of Microprocessor Trend Data Original data up to the year 2010 collected and plotted by M. Horowitz, F. Labonte, O. Shacham, K. Olukotun, L. Hammond, and C. Batten New plot and data collected for 2010-2015 by K. Rupp Single-threaded perf 1.5X per year 1.1X per year Transistors (thousands)
- 5. 5 200B CORE HOURS OF LOST SCIENCE Data Center Throughput is the Most Important Thing for HPC Source: NSF XSEDE Data: https://portal.xsede.org/#/gallery NU = Normalized Computing Units are used to compare compute resources across supercomputers and are based on the result of the High Performance LINPACK benchmark run on each system 0 50 100 150 200 250 300 350 400 2009 2010 2011 2012 2013 2014 2015 Computing Resources Requested Computing Resources Available NormalizedUnit(Billions) National Science Foundation (NSF XSEDE) Supercomputing Resources
- 6. 6 0.0 1.0 2.0 3.0 4.0 5.0 6.0 2008 2009 2010 2011 2012 2013 2014 2016 NVIDIA GPU x86 CPUTFLOPS M2090 M1060 K20 K80 K40 Fast GPU + Strong CPU THE ADVANTAGES OF GPU-ACCELERATED DATA CENTER P100
- 7. 7 1980 1990 2000 2010 2020 GPU-Computing perf 1.5X per year 1000X by 2025 RISE OF GPU COMPUTING Original data up to the year 2010 collected and plotted by M. Horowitz, F. Labonte, O. Shacham, K. Olukotun, L. Hammond, and C. Batten New plot and data collected for 2010-2015 by K. Rupp 102 103 104 105 106 107 Single-threaded perf 1.5X per year 1.1X per year APPLICATIONS SYSTEMS ALGORITHMS CUDA ARCHITECTURE
- 8. 8 U.S. TO BUILD TWO FLAGSHIP SUPERCOMPUTERS Pre-Exascale Systems Powered by the Tesla Platform 100-300 PFLOPS Peak IBM POWER9 CPU + NVIDIA Volta GPU NVLink High Speed Interconnect 40 TFLOPS per Node, >3,400 Nodes 2017 Summit & Sierra Supercomputers
- 9. 9 TEN YEARS OF GPU COMPUTING 2006 2008 2012 20162010 2014 Fermi: World’s First HPC GPU Oak Ridge Deploys World’s Fastest Supercomputer w/ GPUs World’s First Atomic Model of HIV Capsid GPU-Trained AI Machine Beats World Champion in Go Stanford Builds AI Machine using GPUs World’s First 3-D Mapping of Human Genome CUDA Launched World’s First GPU Top500 System Google Outperform Humans in ImageNet Discovered How H1N1 Mutates to Resist Drugs AlexNet beats expert code by huge margin using GPUs
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- 11. 11 OIL & GAS - NVIDIA INDEX Leading HPC Tool to Analyze Large-Scale Data for Faster Discoveries Interactive Built for Large-Scale DataRemote Visualization Performance @ Scale Visualize Anywhere
- 13. 13 “IBM-NVIDIA SERVERS ACHIEVE HIGH- PERFORMANCE COMPUTING MILESTONE IN OIL INDUSTRY” Servers 22,400 Processors 24 Total CPUs 537,600 Servers 30 GPUs 4 Total GPUs 120 https://www.forbes.com/sites/aarontilley/2017/04/25/ibm-nvidia-servers-achieve-high-performance-computing-milestone-in-oil-industry/#8e3b56626330 1 Billion Cells Resservoir Model 25 April 2017 ExxonMobil using the Blue Water facility at NCSA ECHELON – Simulation on GPUs Stone Ridge Technologies
- 14. 14 RESERVOIR SIMULATION Company Simulator/Method Model Production Simulation Runtime Reference Cores/Servers Saudi Aramco GIGAPOWERS Three-phase black oil 1.03 Billion cells 3,000 wells 60 years 4 days [1] Saudi Aramco GIGAPOWERS Three-phase black oil 1.03 Billion cells 3,000 wells 60 years 21 hours [2] 5640 Cores 470 Servers Total/Schlumberger INTERSECT 1.1 Billion cells 361 wells 20 years 10.5 hours [3] 576 Cores 288 Servers ExxonMobil ? 1 Billion cells ? ? ? 716,800 Cores 22,400 Servers StoneRidge Echelon Three-phase black oil 1.01 Billion cells 1,000 wells 45 years 92 minutes ? 120 GPUS 30 Servers Performance Comparison [1] SPE 119272 “A Next-Generation Parallel Reservoir Simulator for Giant Reservoirs”, A. Dogru et. al. 2009 SPE Reservoir Simulation Symposium. [2] SPE 142297 “New Frontiers in Large Scale Reservoir Simulation”, A. Dogru et. al. 2011 SPE Reservoir Simulation Symposium. [3] IPTC 17648 “Giga Cell Compositional Simulation”, E. Obi et. al., 2014 International Petroleum Technology Conference.
- 17. 17 GPU ACCELERATED LIBRARIES “Drop-in” Acceleration for Your Applications Domain-specific Deep Learning, GIS, EDA, Bioinformatics, Fluids Visual Processing Image & Video Linear Algebra Dense, Sparse, Matrix Math Algorithms AMG, Templates, Solvers NVIDIA cuRAND NVIDIA NPPNVIDIA CODEC SDK NVBIO Triton Ocean SDK NVIDIA cuBLAS, cuSPARSE AmgX cuSOLVER developer.nvidia.com/gpu-accelerated-libraries
- 19. 19 LSDalton Quantum Chemistry 12X speedup in 1 week Numeca CFD 10X faster kernels 2X faster app PowerGrid Medical Imaging 40 days to 2 hours INCOMP3D CFD 3X speedup NekCEM Computational Electromagnetics 2.5X speedup 60% less energy COSMO Climate Weather 40X speedup 3X energy efficiency CloverLeaf CFD 4X speedup Single CPU/GPU code MAESTRO CASTRO Astrophysics 4.4X speedup 4 weeks effort
- 20. 20 OPENACC FOR EVERYONE New PGI Community Edition Now Available PROGRAMMING MODELS OpenACC, CUDA Fortran, OpenMP, C/C++/Fortran Compilers and Tools PLATFORMS x86, OpenPOWER, NVIDIA GPU UPDATES 1-2 times a year 6-9 times a year 6-9 times a year SUPPORT User Forums PGI Support PGI Enterprise Services LICENSE Annual Perpetual Volume/Site FREE
- 22. 22 DEEP LEARNING
- 23. 23 LEARNING FROM DATA AND SOME BUZZ WORDS ARTIFICAL INTELLIGENCE MACHINE LEARNING DEEP LEARNING Knowledge & Reason Learning Planning Communicating Perceiving Learning from data Expert systems Handcrafted features Learning from data Neural networks Computer learned features
- 24. 24 A NEW COMPUTING MODEL “Label” Input Training Data Output Trained Neural Network Trained Neural Network “Label” Output Input TRAINING INFERENCE
- 25. 25 A NEW COMPUTING MODEL Outperform experts, facts, rules with software that writes software Deep Learning Object Detection DNN + Data + GPU Traditional Computer Vision Experts + Time Deep Learning Achieves “Superhuman” Results 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 2009 2010 2011 2012 2013 2014 2015 2016 Traditional CV Deep Learning ImageNet
- 26. 26 “ACCELERATING EULERIAN FLUID SIMULATION WITH CONVOLUTIONAL NETWORKS” Tompson, J., Schlachter, K., Sprechmann, P., & Perlin, K. (2016). Accelerating Eulerian Fluid Simulation With Convolutional Networks. arXiv preprint arXiv:1607.03597.
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- 29. 29 Ad Service Technology Investment Media Oil & Gas Mfg Retail Other $500B OPPORTUNITY OVER 10 YRS Deep Learning Software Revenue by Industry Deep Learning Total Revenue by Segment IBM: “Cognitive business represents a $2T opportunity” SOURCE: “Deep Learning for Enterprise Applications,” 4Q 2015, Tractica
- 31. 31 SAP AI FOR THE ENTERPRISE First commercial AI offerings from SAP Brand Impact, Service Ticketing, Invoice-to-Record applications Powered by NVIDIA GPUs on DGX-1 and AWS
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- 35. 35 Uber Enters the Race Toyota Invests $1B in AI Lab Volvo Drive Me on Public Roads in 2017 NHTSA: Computer Counts as Driver Tesla Model 3: 300K pre-orders AN AMAZING YEAR FOR SELF-DRIVING CARS Audi, BMW, Daimler Buy HERE Tesla Model S Auto-pilot Baidu Enters the Race Honda, Nissan, Toyota Team Up GM Buys Cruise
- 36. 36 NEW AI DRIVING Training on DGX-1 Driving with DriveWorks KALDI LOCALIZATION MAPPING DRIVENET DAVENET NVIDIA DGX-1 NVIDIA DRIVE PX
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- 39. 39 POWERING THE DEEP LEARNING ECOSYSTEM NVIDIA SDK accelerates every major framework COMPUTER VISION OBJECT DETECTION IMAGE CLASSIFICATION SPEECH & AUDIO VOICE RECOGNITION LANGUAGE TRANSLATION NATURAL LANGUAGE PROCESSING RECOMMENDATION ENGINES SENTIMENT ANALYSIS DEEP LEARNING FRAMEWORKS Mocha.jl NVIDIA DEEP LEARNING SDK developer.nvidia.com/deep-learning-software
- 40. 40 NVIDIA DIGITS Interactive Deep Learning GPU Training System developer.nvidia.com/digits Interactive deep neural network development environment for image classification and object detection Schedule, monitor, and manage neural network training jobs Analyze accuracy and loss in real time Track datasets, results, and trained neural networks Scale training jobs across multiple GPUs automatically
- 41. 41 OBJECT DETECTION IMAGE CLASSIFICATION DEEP LEARNING WORKFLOWS Classify images into classes or categories Object of interest could be anywhere in the image Find instances of objects in an image Objects are identified with bounding boxes 98% Dog 2% Cat New in DIGITS 5 Partition image into multiple regions Regions are classified at the pixel level IMAGE SEGMENTATION
- 42. 42 DLI – DEEP LEARNING INSTITUTE http://www.nvidia.com/object/deep-learning-institute.html
- 43. 43 BIG DATA & ANALYTICS
- 44. 44 DATA DELUGE TO DATA HUNGRY INCREASING DATA VARIETY Search Marketing Behavioral Targeting Dynamic Funnels User Generated Content Mobile Web SMS/MMS Sentiment HD Video Speech To Text Product/ Service Logs Social Network Business Data Feeds User Click Stream Sensors Infotainment Systems Wearable Devices Cyber Security Logs Connected Vehicles Machine Data IoT Data Dynamic Pricing Payment Record Purchase Detail Purchase Record Support Contacts Segmentation Offer Details Web Logs Offer History A/B Testing BUSINESS PROCESS PETABYTESTERABYTESGIGABYTESEXABYTESZETTABYTES Streaming Video Natural Language Processing WEB DIGITAL AI
- 45. 45 DATA & ANALYTICS USE CASES AUTOMOTIVE Auto sensors reporting location, problems COMMUNICATIONS Location-based advertising CONSUMER PACKAGED GOODS Sentiment analysis of what’s hot, problems $ FINANCIAL SERVICES Risk & portfolio analysis New products EDUCATION & RESEARCH Experiment sensor analysis HIGH TECHNOLOGY / INDUSTRIAL MFG. Mfg. quality Warranty analysis LIFE SCIENCES Clinical trials MEDIA/ENTERTAINMENT Viewers / advertising effectiveness ON-LINE SERVICES / SOCIAL MEDIA People & career matching HEALTH CARE Patient sensors, monitoring, EHRs OIL & GAS Drilling exploration sensor analysis RETAIL Consumer sentiment TRAVEL & TRANSPORTATION Sensor analysis for optimal traffic flows UTILITIES Smart Meter analysis for network capacity, LAW ENFORCEMENT & DEFENSE Threat analysis - social media monitoring, photo analysis
- 46. 46 DGX-1 FOR ANALYTICS SOLUTIONS + ARCHITECTURES Spark Scheduler CORE TECHNOLOGIES GPU-ACCELERATED DATA CENTER ACCELERATED VISUALIZATION ACCELERATED DATABASES DEEP LEARNING CloudNVIDIA DGX Products CORE TECHNOLOGIES TRADITIONAL DATA CENTER VISUALIZATION DATABASES NVIDIA Tesla GPUs Mesos
- 47. 47 GPU-ACCELERATION HAS NO LIMITS MapD BlazeGraph Kinetica Leading In-Memory DB > 50x Slower NoSQL DB’s > 100x Slower Aggregate of queries - Time (s) Less is better! SQream 1403 1843 700 GPUs 700X-800X faster than graphs in all cases 700M Edges Single Node Xeon 2650 vs 2 K80 1.98B Edges 16 EC2 r3.xlarge vs 16 K40s 1.98B Edges 16 EC2 r3.4xlarge vs 16 K40s2 1.98B Edges Spark CPU Baseline 1 Speed-up over baseline spark CPU configuration Speed-up(higherisfaster)
- 48. 48 ACCELERATED ANALYTICS IN ACTION ARCHITECTURE PRODUCT VIEW
- 50. 50 WEAK NODES Lots of Nodes Interconnected with Vast Network Overhead STRONG NODES Few Lightning-Fast Nodes with Performance of Hundreds of Weak Nodes Network Fabric Server Racks
- 51. 51 150B XTORS | 5.3TF FP64 | 10.6TF FP32 | 21.2TF FP16 | 14MB SM RF | 4MB L2 Cache TESLA P100 THE MOST ADVANCED HYPERSCALE DATACENTER GPU EVER BUILT
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- 53. 53 Instant productivity — plug-and- play, supports every AI framework and accelerated analytics software applications Performance optimized across the entire stack Always up-to-date via the cloud Mixed framework environments — baremetal and containerized Direct access to NVIDIA experts DGX STACK Complete Analytics and Deep Learning platform
- 54. 54 Fastest AI Supercomputer in TOP500 #28 Top500 4.9 Petaflops Peak FP64 19.6 Petaflops Peak FP16 Most Energy Efficient Supercomputer #1 Green500 9.5 GFLOPS per Watt Rocket for Cancer Moonshot CANDLE Development Platform Common platform with DOE labs – ANL, LLNL, ORNL, LANL NVIDIA DGX SATURNV Giant Leap Towards Exascale AI
- 55. 55 Key Performance Indicator (KPI) Power Efficiency Source - http://top500.org Rmax (TFLOPS/s) Power (kW) Efficiency NVIDIA DGX Saturn V 3307.0 350 9.45 TaihuLight 93014.6 15371 6.05 Santos Dumont GPU 456.8 371 1.23 Santos Dumont XeonPhi 363.2 859 0.42
- 56. 56 TESLA REVOLUTIONIZES DEEP LEARNING GOOGLE BRAIN APPLICATION BEFORE TESLA AFTER TESLA Cost $5,000K $200K Servers 1,000 Servers 16 Tesla Servers Energy 600 KW 4 KW Performance 1x 6x
- 57. 57 ANNOUNCING TESLA V100 GIANT LEAP FOR AI & HPC VOLTA WITH NEW TENSOR CORE 21B xtors | TSMC 12nm FFN | 815mm2 5,120 CUDA cores 7.5 FP64 TFLOPS | 15 FP32 TFLOPS NEW 120 Tensor TFLOPS 20MB SM RF | 16MB Cache 16GB HBM2 @ 900 GB/s 300 GB/s NVLink
- 58. 58 NEW TENSOR CORE New CUDA TensorOp instructions & data formats 4x4 matrix processing array D[FP32] = A[FP16] * B[FP16] + C[FP32] Optimized for deep learning Activation Inputs Weights Inputs Output Results
- 59. 59 TENSOR CORE 4x4x4 matrix multiply and accumulate
- 60. 60 Tesla P100 vs Tesla V100 Tesla P100 (Pascal) Tesla V100 (Volta) Memory 16 GB (HBM2) 16 GB (HMB2) Memory Bandwidth 720 GB/s 900 GB/s NVLINK 160 GB/s 300 GB/s CUDA Cores (FP32) 3584 5120 CUDA Cores (FP64) 1792 2560 Tensor Cores (TC) NA 640 Peak TFLOPS/s (FP32) 10.6 15 Peak TFLOPS/s (FP64) 5.3 7.5 Peak TFLOPS/s (TC) NA 120 Power 300 W 300 W
- 61. 61 ANNOUNCING NVIDIA DGX-1 WITH TESLA V100 ESSENTIAL INSTRUMENT OF AI RESEARCH 960 Tensor TFLOPS | 8x Tesla V100 | NVLink Hybrid Cube From 8 days on TITAN X to 8 hours 400 servers in a box
- 62. 62 ANNOUNCING NVIDIA DGX STATION PERSONAL DGX 480 Tensor TFLOPS | 4x Tesla V100 16GB NVLink Fully Connected | 3x DisplayPort 1500W | Water Cooled
- 63. 63 Registry of Containers, Datasets, and Pre-trained models NVIDIA GPU CLOUD CSPs ANNOUNCING NVIDIA GPU CLOUD Containerized in NVDocker | Optimization across the full stack Always up-to-date | Fully tested and maintained by NVIDIA | Beta in July GPU-accelerated Cloud Platform Optimized for Deep Learning
- 64. 64 DL APPLICATIONS TO O&G
- 65. 65 WELL-LOG ESTIMATION Korjani, N., Popa, A., Grijalva, E., Cassidy, S., Ershaghi, E. (2016), “A New Approach to Reservoir Characterization Using Deep Learning Neural Networks”, SPE 2016 Univ of South California Chevron North America Exp & Prod SPE Western Regional Meeting, 23-26 May 2016
- 66. 66 WELL-LOG ESTIMATION Input: >20.000 wells Kern River Field, San Joaquim, California. Test: New drilled wells (“A” & “B”). SPE Western Regional Meeting, 23-26 May 2016 WELL “A” WELL “B” DRES 81% 82% GR 80% 81% NPHI 79% 72% Results (Correlation)
- 67. 67 FACIES CLASSIFICATION Deep Learning Approach Hall, B. (2016). “Facies classification using machine learning”. The Leading Edge, October 2016, 906-909.
- 68. 68 FACIES CLASSIFICATION 1) Gamma ray (GR) 2) Resistivity (ILD_log10) 3) Photoelectric effect (PE) 4) Neutron-density porosity difference (DeltaPHI) 5) Average neutron-density porosity (PHIND) 6) Nonmarine/marine indicator (NM_M) 7) Relative position (RELPOS) 8) Depth Deep Learning Approach
- 69. 69 SALT SEGMENTATION DL-based Salt Segmentation on Seismic Images
- 70. 70 SALT SEGMENTATION DL-based Salt Segmentation on Seismic Images
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- 74. 74 NVIDIA HW GRANT PROGRAM Titan X Pascal • Robotics • Autonomous Machines Jetson TX1 (Dev Kit) • Scientific Visualization • Virtual Reality Quadro M5000 • Scientific Computing • HPC • Deep Learning
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- 77. Pedro Mario Cruz e Silva ([email protected]) Solution Architect, Enterprise Latin America More information: developer.nvidia.com