Scaling Data Analytics with GPUs
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Spark Summit EU talk by Andy Steinbach
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Scaling Data Analytics with GPUs
- 1. Spark Summit Brussels, October 26, 2016 THE POTENTIAL OF GPU-DRIVEN HIGH PERFORMANCE DATA ANALYTICS IN SPARK Andy Steinbach, Sr. Director, NVIDIA
- 2. 2 Scale up Compute intensive Scale out Data intensive HOW TO SCALE AI & DATA ANALYTICS? We are headed here
- 3. 3 HIGH PERFORMANCE DATA ANALYTICS Scale out Scale up Spark + TensorFlow + GPU Spark + AI framework + GPU Machine Learning & DB Query Deep Learning
- 4. 4 “Training” ImageNet “Inference” DEEP LEARNING - A NEW COMPUTING MODEL
- 5. 5 BEYOND JUST COMPUTER VISION
- 6. 6 Trained model Labelled training examples Inference applied to unseen inputs A REVOLUTION IN MEDECINE
- 7. 7 A REVOLUTION IN ROBOTICS
- 10. 10 WHAT DOES DEEP LEARNING LEARN? Feature Representation Learning Algorithm Input
- 11. 11 PREDICTIVE ANALYTICS IS NEXT
- 12. 12 10,000s of features make up todays fraudulent behavior. AI can detect patterns faster and more accurate than humans -Hui Wang, Senior Director of Global Risk Sciences, Pay Pal PREDICTIVE ANALYTICS IS NEXT
- 13. 13 THE NEED TO SCALE UP & OUT IS HUGE 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
- 14. 14 DGX-1 DEEP LEARNING SUPERCOMPUTER
- 15. 15 0 100 200 300 400 500 600 700 800 2008 2011 2012 2014 2016 Peak Memory Bandwidth NVIDIA GPU x86 CPU M2090 M1060 K20 K80 Pascal GB/s 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 2008 2010 2012 2014 2016 Peak Double Precision FLOPS NVIDIA GPU x86 CPU M1060 K20 GFLOPS K80 Pascal M2090 PERFORMANCE GAP INCREASES
- 16. 16 0 60 120 180 Scala UDF Scala UDF (optimized) TensorFrames TensorFrames + GPU Runtime(sec) In practice, compute: with: In a nutshell: a complex numerical function HOW TO SCALE DATA ANALYTICS?
Editor's Notes
- The Times are a Changing – Again AI - Next Industrial Revolution What is AI, ML, DL – Blog Training vs Inference - Blog Who needs it (Industries/functions) Industries, Companies, Applications People need to care about AI What you need to be able to do DL Data, HW (GPUs), SW (DLSDK) Pain exists – our solutions address Walk through personas: clinician/digits Why NVIDIA is the platform to succeed in AI Support – SA, IBDs, DLI
- PayPal Problem - Online fraud Solution - The deep learning algorithms are able to analyze potentially tens of thousands of latent features (time signals, actors and geographic location are some easy examples) that might make up a particular type of fraud, and are even able to detect “sub modus operandi,” or different variants of the same scheme, she said. Impact - She’s hopeful deep learning will give her team the ability to adapt to these new patterns faster than before. It’s possible, for example, that PayPal might some day be able to deploy models that take live data from its system and become smarter, by retraining themselves, in real time. https://gigaom.com/2015/03/06/how-paypal-uses-deep-learning-and-detective-work-to-fight-fraud/
- Increase data size/variety