getting cloudy with remote graphics and gpu compute using g2 instances (cpn210) | aws re:invent 2013
DESCRIPTION
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.TRANSCRIPT
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© 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.
GPU Instances on Amazon EC2
John Phillips, Sr. Product Manager, Amazon EC2
November 15, 2013
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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)
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Instance Types Today
2006 2007 2008 2009 2010 2011 2012 2013
m1.small
m1.xlarge
m1.large
m1.small
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
cc1.4xlarge
cg1.4xlarge
t1.micro
m2.xlarge
m2.2xlarge
m2.4xlarge
c1.medium
c1.xlarge
m1.xlarge
m1.large
m1.small
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
c1.medium
c1.xlarge
m1.xlarge
m1.large
m1.small
new
existing Entry into GPU
space
G2 Instances
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Why GPUs? Parallel Performance
Product 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**
Peak Gflops (double) 166* 1,310***
Total Memory >>4GB 6GB
* 256-bit AVX addition + 256 AVX multiplication /cycle/core ** 32-bit FMA /cycle/core *** 64-bit FMA /cycle/2core
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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
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Customer Feedback
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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
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$0.65 per hour
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Why remote graphics in Amazon EC2?
Accessibility
Quality of service
Business agility
Collaboration
Data security
And…
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AWS Under Your Desk
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© 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.
GPU Instances on AWS:
Desktop Apps Are The New Web Apps
Gyuri Ordody, Autodesk
November 15, 2013
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About Autodesk
Autodesk started more than 30 years ago,
with 16 employees and one software title.
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Today: an industry leader in design software for the building, manufacturing,
infrastructure and entertainment industries
About Autodesk
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Architecture, Engineering and Construction
Image courtesy of Castro Mello Architects Image courtesy of Castro Mello Architects
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Image courtesy of Brimrock Group Inc. and Mechanix Design Solutions Inc.
Digital Prototyping
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Media & Entertainment
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CAD Evolution
IBM PC 5150 with keyboard and green monochrome monitor (5151), running MS-DOS 5.0 © Boffy
b
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Lines, Arcs, Circles Features, Shapes, Blocs Intelligent Objects
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CAD Evolution
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Image courtesy of Hunt Construction Group and SHoP
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Huge Datasets
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Simulation, Analysis
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Design = Visualization
• High-end desktop
workstation – CPU (Xeon multicore)
– RAM (16GB)
– GPU (DirectX 9-11)
– Fast Disk
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Design = Collaboration
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+
Design = Collaboration
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Design Graph +
Design = Collaboration
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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
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Collaboration – Using the AWS Cloud
• Access it anywhere
• Access using any device
• Seamless collaboration
• Editing from anywhere
• Data close to application
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Desktop Apps Home / Office
Player
Application Remoting Overview
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Bitmap / Video
Keyboard,
mouse, USB
Internet
EC2 Instance
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EC2 Instance
Home / Office
Player
Application Remoting Overview
Bitmap / Video
Keyboard,
mouse, USB
Internet
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Desktop Apps
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EC2 Instance
Home / Office
Player
Application Remoting Overview
Bitmap / Video
Keyboard,
mouse, USB
Internet
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Desktop Apps
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Autodesk Online Application - Architecture
Internet
User
Client
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Region 1
Controller
EC2 Instance
…
Region 2
Controller
EC2 Instance
Region N
Controller
EC2 Instance
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Autodesk Online Application - Architecture
Internet
User
Client
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Application Settings
Default User Data
SimpleDB
User Data
Session Data
Region
App Servers
EC2 Instances
App Server AMI
Connection
Controller
EC2 Instance
S3
Custom Scaling
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Autodesk Desktop Apps
EC2 Instance
Home / Office 1
Application Remoting – Instance sharing
Internet
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Home / Office n
Internet
…
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Data Sets
Desktop Applications – Data Exchange N GB/exchange
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Data references
Cloud data exchange – Predictable Data Traffic
Data references
N kb/exchange
N kb/exchange
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Data references
Cloud data exchange – Predictable Data Traffic
N kb/exchange
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Data
references +
Video Stream
1 GB/hr
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A360 and the AWS Cloud
Supporting
Infrastructure Identity
EC2 + RDS
Storage
S3 DynamoDB
Simulation
EC2 EMR
Analysis
EC2 EMR RDS
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Application + Cloud Integration
User
Ec2 + RDB
Autodesk
Identity
Service
S3 +
DB
Autodesk
Storage
Service
AWS Cloud
Autodesk
Desktop Apps
Internet
Client
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Application + Cloud Integration
User
EC2 + RDB
Identity
S3 +
DB
Storage
AWS Cloud
Internet
Client
Autodesk
Desktop
Apps
EC2
No
GPU
Autodesk
App
Player
Autodesk
Desktop Apps
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Application + Cloud Integration
User
EC2 + RDB
Identity
S3 +
DB
Storage
AWS Cloud
Internet
Client
Autodesk
Desktop
Apps
EC2
Autodesk
App Player
GPU!
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Autodesk Apps in the Cloud Without Amazon EC2 - GPU Instances
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Autodesk Apps in the Cloud With Amazon EC2 - GPU Instances
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Application + Cloud Integration
User
EC2 + RDB
Identity
S3 +
DB
Storage
AWS cloud
Internet
Client
Autodesk
Desktop
Apps
EC2
Autodesk
App Player
GPU!
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Application + Cloud Integration
User
EC2 + RDB
Identity
S3 +
DB
Storage
AWS cloud
Internet
Client
Autodesk
Desktop
Apps
EC2
* GPU!
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Application + Cloud Integration
Identity
Storage
AWS cloud
Autodesk
Desktop
Apps
EC2
Instances
Search
Translation
Simulation Analysis
GPU!
Supporting
Infrastructure
Internet
User
Client
*
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Demo: Running Design Apps
In a Browser
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Demos
• Live
• YouTube: http://www.youtube.com/watch?v=lU85EjvTyz0
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© 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 on AWS
Teng Lin, Senior Principal Scientist, Schrödinger
November 15, 2013
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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
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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
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Schrödinger
• Providing software solutions and services for life
sciences and materials research
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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.
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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
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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
18.5
109.8
60.8
86.4
4.9
26.3
15.2 21.0
0.0
20.0
40.0
60.0
80.0
100.0
120.0
8 x Intel Xeon X5672 GeForce GTX780 Amazon Tesla M2050 Amazon Geforce GridK520
Sp
ee
d (
ns
/da
y)
DHFR
APOA1
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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”
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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
$16.61
$28.28
$12.67
$32.01
$75.60
$3.78
$17.62
$32.76
$0.00
$20.00
$40.00
$60.00
$80.00
GTX-780 (50%util)
Tesla K20 (70%util)
Spot InstanceCG1
3-yr HEAVYreserved (100%
util) CG1
On-demandInstance CG1
Spot InstanceG2
3-yr HEAVYreserved (100%
util) G2
On-demandInstance G2
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• Next version will be cloud oriented
• Data will be processed and visualized on cloud
FEP on cloud
Client
Mobile Client
Amazon EC2
Web Servers
Traditional ServerCorporate
Data Center
VPN Gateway VPN Connection
VPN
Connection
DB Instance ClusterGPU Cluster
Internet
Gateway
Auto Scaling
Auto Scaling
![Page 58: Getting Cloudy with Remote Graphics and GPU Compute Using G2 instances (CPN210) | AWS re:Invent 2013](https://reader036.vdocuments.net/reader036/viewer/2022062319/554e8fdbb4c90573338b4d07/html5/thumbnails/58.jpg)
Retrospective Study
-14
-13
-12
-11
-10
-9
-8
-7
-6
-5
-4
-14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4
y = 1.07x + 0.652; R² = 0.599
(Linear regression to all ~150 ligands
across multiple systems)
Binding Affinity (kcal/mol)
Bin
din
g A
ffin
ity
Pre
dic
tio
n (
kc
al/
mo
l)
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• 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 a contract after the test
6 3 1
19
88
66
59
48
0
10
20
30
40
50
60
70
80
90
Highest Lowest
Co
un
t
Binding Affinity
Blind test with company X on AWS
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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
8 1 1
19
10 10
4
1
0
2
4
6
8
10
12
14
16
18
20
Highest Lowest
Co
un
t
Binding Affinity
![Page 61: Getting Cloudy with Remote Graphics and GPU Compute Using G2 instances (CPN210) | AWS re:Invent 2013](https://reader036.vdocuments.net/reader036/viewer/2022062319/554e8fdbb4c90573338b4d07/html5/thumbnails/61.jpg)
Prospective FEP with Company Z on AWS
• 1/3 of molecules are active instead of 1/7
• Company Z uses FEP on many projects
10
23
4
19
9
4 4
2 1 1
0 0 0 0 1
2
0 1
0
5
10
15
20
25
Highest Lowest
Co
un
t
Binding Affinity
Non-FEP
FEP predict to be active
FEP predict to be inactive
![Page 62: Getting Cloudy with Remote Graphics and GPU Compute Using G2 instances (CPN210) | AWS re:Invent 2013](https://reader036.vdocuments.net/reader036/viewer/2022062319/554e8fdbb4c90573338b4d07/html5/thumbnails/62.jpg)
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
![Page 63: Getting Cloudy with Remote Graphics and GPU Compute Using G2 instances (CPN210) | AWS re:Invent 2013](https://reader036.vdocuments.net/reader036/viewer/2022062319/554e8fdbb4c90573338b4d07/html5/thumbnails/63.jpg)
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