1) Utility access to computing power through dynamic, on-demand cloud infrastructure allows scientists to ask bigger questions and accelerate research by running experiments that previously took months or years to complete in just hours or days. 2) Examples of use cases that benefit from utility HPC include drug design through molecular modeling, CAD/CAM engineering simulations, genomic analysis of stem cell RNA, and nuclear power plant safety simulations. 3) Automating cloud infrastructure provisioning and workload management through tools like Chef allows researchers to easily create large supercomputer clusters of tens of thousands of cores on demand for a fraction of the cost of traditional HPC systems.

1. Utility HPC:
Right Systems, Right Scale,
Right Science
Jason Stowe, CEO
@jasonastowe, @cyclecomputing

2. I’m here to recruit you,
for a cause

3. We believe
utility access to compute power
makes impossible science,
possible.

4. Dynamic, utility access to
compute power
is as important as uptime

5. (that’s why coded infrastructure
is critical)

6. Skeptical?
Flickr:
Tourist
on
Earth

7. In prior years (today?)
Researchers/engineers waited
for computing

8. For
the
horsepower

9. For
the
place
to
put
it

10. For
it
to
be
Configured..
Flickr: vaxomatic

11. Yesterday, high performance
engineering, science clusters
were…
Too small
when you need it most,
Too large
every other time.

12. The Innovation Bottleneck:
Researchers/Scientists/Engineers
Forced to size questions to the
infrastructure you have

13.
Multi-­‐tenant
systems
create
float
capacity
That
is
critical
to
innovation

14. The
60’s
The
70’s
The
80’s
The
90’s
The
00’s
From centralized to decentralized, collaborative to independent
and right back again!
The
10’s
Mainframes VAX
The
PC
Beowulf Clusters Central
Clouds
100% 60% 0% 40% ??? %
SHARING
~
0Mbit
~ 1Mbit ~ 10Mbit ~
1000
Mbit
~ 10,000 Mbit
Bigger, better but further and further away from the scientist’s lab

15. Ask a
Question Hypothesize Predict
Experiment /
Test Analyze Final Results
The Scientific Method
Test and Analyze stages
require the most time,
compute, and data

16. Ask a
Question Hypothesize Predict
Experiment /
Test Analyze Final Results
The Scientific Method
Any improvements to this
cycle yield multiplicative
benefits

17. A Challenge Across Industries
— 3 of Top 5 Insurance
— 6 of Top 8 Pharmaceutical
— 2 of Top 3 Banks
— 2 of Top 3 Genomics Sequencing
— 1 of Top 2 FPGA

18. Utility HPC in the News
WSJ, NYTimes, Wired, Bio-IT World BusinessWeek

19. To accelerate science, we need
automation

21. Management Software
CC1/CCG
Instances
EBS
S3
Shared
FS
EBS
Utility
HPC
Cluster
-­‐ Scales
to
50,000+
cores
-­‐ Data
Scheduling
-­‐ Workload
portability
Data &
Application
Aware
Movement
Traditional
Scheduler
Massive Scale
Based upon workload
Secure, HPC
Cluster
User
HPC
Reporting &
Audit

22. 50,000-core CycleCloud
Using Chef and AWS
ChefConf 2012

23. 10,600-instance cluster
against cancer target
ChefConf 2013

24. Created in 2 hours
Configured with Search,
with Data bags

25. one Chef 11 server

26. We make software tools to easily orchestrate complex
workloads and data access across Utility HPC
Today is a survey of use cases…
10,600 instance
Life Science
Molecular
Modeling
600 core
Manufacturing
Nuclear Power
Plant for safety
simulation
Genomic
Analysis
RNA for
Stem Cells

27. Dynamic, utility access to
compute power
is as important as uptime

28. Why?

29. #1: “Better” Science =
“Answer the question we want to
ask”, not constrained to what fits
on local compute power

30. #2 “Faster” Science =
Run this “better” science,
that would have taken
months or years
in hours or days

31. Survey of Use Cases
þ Drug Design
þ CAD/CAM
þ Genomics
…

32. Life Sciences & Compute?
Compute
Data/Bandwidth
Genomics
Molecular
Modeling
CAD/
CAM
All Sample
Analysis
Proteomics
Biomarker/
Image Analysis
Sensor Data Import
Creating fake
Charts, with
Fake Data

33. Why is this important?

34. (W.H.O./Globocan 2008)

35. ~2 million Type 2 diabetics,
~200k Type 1

36. Every day is
crucial and costly

37. Before:
Trade-off compute time vs.
accuracy
Now:
Accurate analysis, fewer false
negatives, faster
Initial
Coarse
Screen
Higher
Quality
Analysis
Best
Quality
Process for Drug Design
Higher
Quality
Analysis
Best
Quality

38. Big 10 Pharma
Built 10,600 instance cluster
($44M) in 2 hours, ran
40 years of science
in 11 hours for $4,372

39. Most Recent Utility Supercomputer
server count:

40. AWS Console view:

41. Cycle’s view of this cluster:
One Chef 11 Server

42. Earlier Drug Design
Novartis discussed at BioIT2012
— Needed
— Push-button Utility Supercomputer for molecular
modeling
— Created
— 30,000 core run across US/EU Cloud (AWS)
— 10 years of compute in 8 hours for $10,000
— Found 3 compounds now in the wetlab as a result

43. — Capacity is no longer an issue
— Hardware = software
— Testing (error handling, unit testing, etc.)
e.g. Cycle spent ~$1M dollars on AWS over 5 years
— The only way to do this is to automate
Lessons learned

44.
Servers
are
not
house
plants

45.
Servers
are
wheat

46. Survey of Use Cases
þ Drug Design
þ CAD/CAM
þ Genomics
…

47. Nuclear Power Plant simulation

48. We don’t’ know what they’re
running, but it has “Safety”

49. 600-core CAD/CAM
3 Quarters of a year wait became 3 weeks
Site
Data
Corporate
Firewall
3 Weeks instead
Of 3 Quarters
Secure
HPC
Cluster
TBs FS
External Cloud
~600 CPU cluster
Scheduled
Data
Engineer

50. Survey of Use Cases
þ Drug Design
þ CAD/CAM
þ Genomics
…

51. Gene Expression Analysis
Morgridge Institute for Research
Run holistic comparison of all 78 terabyte stem cell
RNA samples to build a unique gene expression
database
Make it easier to replicate disease in petri dishes w/
induced stem cells

52. 78 TB of Stem Cell RNA

53. 1 Million compute hours,
115 years of computing in
1 week for $19,555

54. Gene Expression Analysis
Morgridge Institute for Research
— Cluster details
— 5,000 to 10,000 cores for a week
— Very long individual analysis were check-pointed =
Spot instance usage possible

55. Survey of Use Cases
þ Drug Design
þ CAD/CAM
þ Genomics
…

56. Code can accelerate Science

57. Ask a
Question Hypothesize Predict
Experiment /
Test Analyze Final Results
The Scientific Method on Utility HPC
Yield “Better”, “Faster”
Research for less $

58. Dynamic, utility access to
compute power
is as important as uptime

59. I’m here to recruit you,
for a cause

60. Contribute to Chef.
Make the community better.
And you will help Cycle
make impossible science,
possible.

61. 2013 BigScience Challenge
$10,000 of free computing to science
benefitting humanity
2012 winner: 115yr Genomic analysis
Enter at:
http://cyclecomputing.com/big-science-challenge/enter

62. Thank You! Questions?