Getting Cloudy with Remote Graphics and GPU Compute Using G2 instances (CPN210) | AWS re:Invent 2013
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Getting Cloudy with Remote Graphics and GPU Compute Using G2 instances (CPN210) | AWS re:Invent 2013

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Amazon EC2 now offers a new GPU instance capable of running graphics and GPU compute workloads. In this session, we take a deeper look at the remote graphics capabilities of this new GPU instance, the ...

Amazon EC2 now offers a new GPU instance capable of running graphics and GPU compute workloads. In this session, we take a deeper look at the remote graphics capabilities of this new GPU instance, the tooling required to get started, and a live demo of applications streamed from our West Coast regions. We also explore the benefits of hosting your 3D graphics applications in the AWS cloud, where you can harness the vast compute and storage resources.

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Getting Cloudy with Remote Graphics and GPU Compute Using G2 instances (CPN210) | AWS re:Invent 2013 Getting Cloudy with Remote Graphics and GPU Compute Using G2 instances (CPN210) | AWS re:Invent 2013 Presentation Transcript

  • GPU Instances on Amazon EC2 John Phillips, Sr. Product Manager, Amazon EC2 November 15, 2013 © 2013 Amazon.com, Inc. and its affiliates. All rights reserved. May not be copied, modified, or distributed in whole or in part without the express consent of Amazon.com, Inc.
  • Agenda Overview of GPU Instances Gyuri Ordody with Autodesk: Evolution of CAD on AWS Teng Lin with Schrodinger: Drug Discovery on AWS Questions from audience (if there’s time)
  • Instance Types Today G2 Instances new existing m1.small m1.xlarge m1.large m1.small 2006 2007 Entry into GPU space c1.medium c1.xlarge m1.xlarge m1.large m1.small m2.2xlarge m2.4xlarge c1.medium c1.xlarge m1.xlarge m1.large m1.small cc1.4xlarge cg1.4xlarge t1.micro m2.xlarge m2.2xlarge m2.4xlarge c1.medium c1.xlarge m1.xlarge m1.large m1.small cc2.8xlarge cc1.4xlarge cg1.4xlarge t1.micro m2.xlarge m2.2xlarge m2.4xlarge c1.medium c1.xlarge m1.xlarge m1.large m1.small hs1.8xlarge m3.xlarge m3.2xlarge hi1.4xlarge m1.medium cc2.8xlarge cc1.4xlarge cg1.4xlarge t1.micro m2.xlarge m2.2xlarge m2.4xlarge c1.medium c1.xlarge m1.xlarge m1.large m1.small 2008 2009 2010 2011 2012 c3.large c3.xlarge c3.2xlarge c3.4xlarge c3.8xlarge i2.large i2.xlarge i2.2xlarge i2.4xlarge i2.8xlarge g2.2xlarge cr1.8xlarge hs1.8xlarge m3.xlarge m3.2xlarge hi1.4xlarge m1.medium cc2.8xlarge cc1.4xlarge cg1.4xlarge t1.micro m2.xlarge m2.2xlarge m2.4xlarge c1.medium c1.xlarge m1.xlarge m1.large m1.small 2013
  • Why GPUs? Parallel Performance Example CPU Example GPU Coprocessor Processing cores 8 2,688 Clock frequency 2.6GHz 732MHz Memory bandwidth 51.2 GB/s / socket 250GB/s (DDR5) Peak Gflops (single) 333* 3,950** Product Peak Gflops (double) Total Memory 166* 1,310*** >>4GB 6GB * 256-bit AVX addition + 256 AVX multiplication /cycle/core ** 32-bit FMA /cycle/core *** 64-bit FMA /cycle/2core
  • cg1.4xlarge 2 x NVIDIA GF104 GPU (Fermi / Tesla) Intel Xeon X5570 16 vCPUs, 22.5 GiB of RAM 2 x 840 GB storage 10 Gbps NIC
  • Customer Feedback
  • g2.2xlarge 1 NVIDIA GK104 GPU (Kepler / GRID) 2.6 GHz Sandy Bridge CPU w/ Turbo enabled 8 vCPUs, 15 GiB of RAM 60GB SSD storage EBS-Optimized up to 1Gbps Frame Capture and Encoding APIs
  • $0.65 per hour
  • Why remote graphics in Amazon EC2? Accessibility Quality of service Business agility Collaboration Data security And…
  • AWS Under Your Desk
  • GPU Instances on AWS: Desktop Apps Are The New Web Apps Gyuri Ordody, Autodesk November 15, 2013 © 2013 Amazon.com, Inc. and its affiliates. All rights reserved. May not be copied, modified, or distributed in whole or in part without the express consent of Amazon.com, Inc.
  • About Autodesk Autodesk started more than 30 years ago, with 16 employees and one software title. 12
  • About Autodesk Today: an industry leader in design software for the building, manufacturing, infrastructure and entertainment industries 13
  • Architecture, Engineering and Construction Image courtesy of Castro Mello Architects Image courtesy of Castro Mello Architects 14
  • Digital Prototyping Image courtesy of Brimrock Group Inc. and Mechanix Design Solutions Inc. 15
  • Media & Entertainment 16
  • CAD Evolution IBM PC 5150 with keyboard and green monochrome monitor (5151), running MS-DOS 5.0 © Boffy b 17
  • CAD Evolution Lines, Arcs, Circles Features, Shapes, Blocs Intelligent Objects 18
  • Huge Datasets Image courtesy of Hunt Construction Group and SHoP 19
  • Simulation, Analysis 20
  • 21
  • Design = Visualization • High-end desktop workstation – – – – CPU (Xeon multicore) RAM (16GB) GPU (DirectX 9-11) Fast Disk 22
  • Design = Collaboration 23
  • Design = Collaboration + 24
  • Design = Collaboration + Design Graph 25
  • Strategies • Create new cloud services on server clusters – Write or rewrite from scratch • Move desktop technology to headless server technology – EC2 instances and Amazon S3 as backend – Recreate UI functionality in the browser • Deploy existing desktop apps in the cloud – Reuse engine and GUI 26
  • 27
  • Collaboration – Using the AWS Cloud • • • • • Access it anywhere Access using any device Seamless collaboration Editing from anywhere Data close to application 28
  • Application Remoting Overview Home / Office Desktop Apps Internet Keyboard, mouse, USB Player Bitmap / Video EC2 Instance 29
  • Application Remoting Overview Home / Office Desktop Apps Internet Keyboard, mouse, USB Player Bitmap / Video EC2 Instance 30
  • Application Remoting Overview Home / Office Desktop Apps Internet Keyboard, mouse, USB Player Bitmap / Video EC2 Instance 31
  • Autodesk Online Application - Architecture Internet Controller EC2 Instance … Client Region 1 User Controller EC2 Instance Region N Controller EC2 Instance Region 2 32
  • Autodesk Online Application - Architecture Internet Controller EC2 Instance Client App Servers Application Settings User Data EC2 Instances Default User Data Session Data Connection S3 SimpleDB User Custom Scaling App Server AMI Region 33
  • Application Remoting – Instance sharing Home / Office 1 Autodesk Desktop Apps Internet … Home / Office n EC2 Instance Internet 34
  • Desktop Applications – Data Exchange N GB/exchange Data Sets 35
  • Cloud data exchange – Predictable Data Traffic Data references N kb/exchange Data references N kb/exchange 36
  • Cloud data exchange – Predictable Data Traffic Data references N kb/exchange Data references + Video Stream 1 GB/hr 37
  • A360 and the AWS Cloud Identity EC2 + RDS Storage S3 DynamoDB Supporting Infrastructure Analysis EC2 EMR RDS Simulation EC2 EMR 38
  • Application + Cloud Integration Internet Autodesk Desktop Apps Autodesk Identity Service Ec2 + RDB Autodesk Storage Service User Client S3 + DB AWS Cloud 39
  • Application + Cloud Integration Internet Autodesk Desktop Apps Autodesk App Player Autodesk Desktop Apps Identity EC2 + RDB No GPU Storage User Client S3 + DB EC2 AWS Cloud 40
  • Application + Cloud Integration Internet Autodesk App Player Autodesk Desktop Apps Identity EC2 + RDB GPU! Storage User Client S3 + DB EC2 AWS Cloud 41
  • Autodesk Apps in the Cloud Without Amazon EC2 - GPU Instances 42
  • Autodesk Apps in the Cloud With Amazon EC2 - GPU Instances 43
  • Application + Cloud Integration Internet Autodesk App Player Autodesk Desktop Apps Identity EC2 + RDB GPU! Storage User Client S3 + DB EC2 AWS cloud 44
  • Application + Cloud Integration Internet Autodesk Desktop Apps Identity * EC2 + RDB GPU! Storage User Client S3 + DB EC2 AWS cloud 45
  • Application + Cloud Integration Internet Identity Autodesk Desktop Apps Supporting Infrastructure Storage Translation * GPU! Search User Client Simulation EC2 Instances AWS cloud 46 Analysis
  • Demo: Running Design Apps In a Browser Demos • Live • YouTube: http://www.youtube.com/watch?v=lU85EjvTyz0 47
  • Drug Discovery on AWS Teng Lin, Senior Principal Scientist, Schrödinger November 15, 2013 © 2013 Amazon.com, Inc. and its affiliates. All rights reserved. May not be copied, modified, or distributed in whole or in part without the express consent of Amazon.com, Inc.
  • Drug discovery and development stages • It takes $800 Million to $1 Billion and 10 to 15 years to develop a blockbuster http://www.innovation.org/drug_discovery/objects/pdf/RD_Brochure.pdf
  • Simple facts about drug discovery • Each development candidate has a value of $50-100M • But the overhead of producing these in pharmaceutical company is $35-70M – Success rate is only 1 in 3 – Thousands of molecules synthesized • Pharmaceutical Industry needs to overcome the innovation deficit in drug discovery process
  • Schrödinger • Providing software solutions and services for life sciences and materials research
  • Ligand-protein binding • Altering receptor protein conformation, and consequently changing biological functions. • Binding affinity is critical for drug discovery Yibing Shan etal Journal of the American Chemical Society, vol. 133, no. 24, 2011, pp. 9181–9183.
  • Free Energy Perturbation (FEP) • Schrödinger’s FEP product – Can predict binding affinity very accurately • Key features – – – – – – Better sampling algorithm High quality Force Field Perturbation network Automated workflow GPU support Cloud capable
  • GPU is significantly faster • Each edge takes 3 or more days on 96 cores – Slow and unreliable due to cross node communication – Perturbation network makes it even worse 120.0 109.8 Speed (ns/day) 100.0 86.4 80.0 60.8 60.0 DHFR APOA1 40.0 26.3 20.0 18.5 15.2 21.0 4.9 0.0 8 x Intel Xeon X5672 GeForce GTX780 Amazon Tesla M2050 Amazon Geforce Grid K520
  • How can FEP help drug discovery? • Traditional drug design – Takes weeks or even months to synthesize a compound – Costs $1,000 to $5,000 per compound – Synthesize thousands of compounds per project • In-silico design using FEP – Takes 72 GPU hours (~6 hours per calculation with 12 GPUs) – Costs about $75, and the price keeps going down – “want to do 1000 calculations per day”
  • Why AWS? • Scalability – Performed virtual screening using 50,000-core on AWS • Security • Price per FEP job – It takes us two months to get GTX-780 cluster up running $75.60 $80.00 $60.00 $40.00 $20.00 $16.61 $32.76 $32.01 $28.28 $17.62 $12.67 $3.78 $0.00 GTX-780 (50% Tesla K20 (70% Spot Instance 3-yr HEAVY On-demand util) util) CG1 reserved (100% Instance CG1 util) CG1 Spot Instance 3-yr HEAVY G2 reserved (100% util) G2 On-demand Instance G2
  • FEP on cloud • Next version will be cloud oriented • Data will be processed and visualized on cloud Auto Scaling Mobile Client Cluster DB InstanceScaling GPU Cluster Auto Internet Gateway Client Amazon EC2 VPN Gateway Web Servers VPN Connection VPN Connection Traditional Server Corporate Data Center
  • Retrospective Study Binding Affinity Prediction (kcal/mol) -4 -5 -6 -7 -8 -9 -10 -11 -12 y = 1.07x + 0.652; R² = 0.599 (Linear regression to all ~150 ligands across multiple systems) -13 -14 -14 -13 -12 -11 -10 -9 -8 -7 Binding Affinity (kcal/mol) -6 -5 -4
  • Blind test with company X on AWS • 9 out of top 10 are active compounds – Probability of achieving result this good is <1% – “make half as many compounds” – “save years of time on the project” • Company X signed a88 contract after the test 90 Count 80 70 60 50 40 30 20 10 0 66 59 48 19 6 Highest 3 1 Binding Affinity Lowest
  • Blind test with company Y on AWS • 8 out of top 10 are the most active compounds – Probability of achieving result this good is <1% • Company Y wants us to provide a turn key solution 20 19 18 16 Count 14 12 10 10 10 8 6 4 4 1 2 8 0 Highest 1 1 Binding Affinity Lowest
  • Prospective FEP with Company Z on AWS • 1/3 of molecules are active instead of 1/7 • Company Z uses FEP on many projects 25 23 19 Count 20 15 10 Non-FEP 10 9 FEP predict to be active FEP predict to be inactive 5 4 4 2 0 0 Highest 0 4 1 1 1 Binding Affinity 2 0 0 0 1 Lowest
  • Summary • Computer aid drug design plays a critical role in drug discovery • Combining with GPU computing, accurate modeling tools like FEP will accelerate the drug discovery process • Cloud is a viable solution for high performance computing, in terms of pricing and scalability • Amazon is the leader for GPU computing at cloud
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