This document summarizes a presentation given at Amazon about GPU instances on Amazon EC2. The presentation covered: - An overview of Amazon's GPU instance offerings over time, including new G2 instances. - How GPUs enable parallel processing that can accelerate applications. - Examples of how Autodesk has used AWS GPU instances to enable remote desktop applications over the internet, allowing for improved collaboration. - How the life sciences company Schrodinger uses AWS GPU instances to perform drug discovery workloads like free energy perturbation calculations, allowing them to scale simulations and reduce costs compared to owning hardware.

1. 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.

2. 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)

3. 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

4. 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

5. 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

6. Customer Feedback

7. 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

8. $0.65 per hour

9. Why remote graphics in Amazon EC2?
Accessibility
Quality of service
Business agility
Collaboration
Data security
And…

10. AWS Under Your Desk

11. 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.

12. About Autodesk
Autodesk started more than 30 years ago,
with 16 employees and one software title.
12

13. About Autodesk
Today: an industry leader in design software for the building, manufacturing,
infrastructure and entertainment industries
13

14. Architecture, Engineering and Construction
Image courtesy of Castro Mello Architects
Image courtesy of Castro Mello Architects
14

15. Digital Prototyping
Image courtesy of Brimrock Group Inc. and Mechanix Design Solutions Inc.
15

16. Media & Entertainment
16

17. CAD Evolution
IBM PC 5150 with keyboard and green monochrome monitor (5151), running MS-DOS 5.0 © Boffy
b
17

18. CAD Evolution
Lines, Arcs, Circles
Features, Shapes, Blocs
Intelligent Objects
18

19. Huge Datasets
Image courtesy of Hunt Construction Group and SHoP
19

20. Simulation, Analysis
20

21. 21

22. Design = Visualization
• High-end desktop
workstation
–
–
–
–
CPU (Xeon multicore)
RAM (16GB)
GPU (DirectX 9-11)
Fast Disk
22

23. Design = Collaboration
23

24. Design = Collaboration
+
24

25. Design = Collaboration
+
Design Graph
25

26. 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. 27

28. Collaboration – Using the AWS Cloud
•
•
•
•
•
Access it anywhere
Access using any device
Seamless collaboration
Editing from anywhere
Data close to application
28

29. Application Remoting
Overview
Home / Office
Desktop Apps
Internet
Keyboard,
mouse, USB
Player
Bitmap / Video
EC2 Instance
29

30. Application Remoting
Overview
Home / Office
Desktop Apps
Internet
Keyboard,
mouse, USB
Player
Bitmap / Video
EC2 Instance
30

31. Application Remoting
Overview
Home / Office
Desktop Apps
Internet
Keyboard,
mouse, USB
Player
Bitmap / Video
EC2 Instance
31

32. Autodesk Online Application - Architecture
Internet
Controller
EC2 Instance
…
Client
Region 1
User
Controller
EC2 Instance
Region N
Controller
EC2 Instance
Region 2
32

33. 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

34. Application Remoting – Instance sharing
Home / Office 1
Autodesk Desktop Apps
Internet
…
Home / Office n
EC2 Instance
Internet
34

35. Desktop Applications – Data Exchange
N GB/exchange
Data Sets
35

36. Cloud data exchange – Predictable Data Traffic
Data references
N kb/exchange
Data references
N kb/exchange
36

37. Cloud data exchange – Predictable Data Traffic
Data references
N kb/exchange
Data
references +
Video Stream
1 GB/hr
37

38. A360 and the AWS Cloud
Identity
EC2 + RDS
Storage
S3 DynamoDB
Supporting
Infrastructure
Analysis
EC2
EMR
RDS
Simulation
EC2
EMR
38

39. Application + Cloud Integration
Internet
Autodesk
Desktop Apps
Autodesk
Identity
Service
Ec2 + RDB
Autodesk
Storage
Service
User
Client
S3 +
DB
AWS Cloud
39

40. 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

41. Application + Cloud Integration
Internet
Autodesk
App Player
Autodesk
Desktop
Apps
Identity
EC2 + RDB
GPU!
Storage
User
Client
S3 +
DB
EC2
AWS Cloud
41

42. Autodesk Apps in the Cloud
Without Amazon EC2 - GPU Instances
42

43. Autodesk Apps in the Cloud
With Amazon EC2 - GPU Instances
43

44. Application + Cloud Integration
Internet
Autodesk
App Player
Autodesk
Desktop
Apps
Identity
EC2 + RDB
GPU!
Storage
User
Client
S3 +
DB
EC2
AWS cloud
44

45. Application + Cloud Integration
Internet
Autodesk
Desktop
Apps
Identity
*
EC2 + RDB
GPU!
Storage
User
Client
S3 +
DB
EC2
AWS cloud
45

46. Application + Cloud Integration
Internet
Identity
Autodesk
Desktop
Apps
Supporting
Infrastructure
Storage
Translation
*
GPU!
Search
User
Client
Simulation
EC2
Instances
AWS cloud
46
Analysis

47. Demo: Running Design Apps
In a Browser
Demos
• Live
• YouTube: http://www.youtube.com/watch?v=lU85EjvTyz0
47

48. 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.

49. 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

50. 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

51. Schrödinger
• Providing software solutions and services for life
sciences and materials research

52. 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.

53. 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

54. 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

55. 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”

56. 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

57. 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

58. 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

59. 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

60. 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

61. 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

62. 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

63. Please give us your feedback on this
presentation
CPN210
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