This session will give you practical approaches to efficiently and securely manage your data using AWS, including ready-made HPC tools and services to quickly get started analyzing that data. We will demonstrate using Alces Flight to interact with and process aerial imagery data to build geographic data sets. We’ll also point to the future of new serverless architectures like AWS Lambda to compute at massive scale. Speakers: Adrian White – Technical Business Development Lead, Research & Technical Computing, Asia Pacific, AWS Dr Matthew Berryman – Managing Director, Across the Cloud Pty Ltd and Chair, University of Wollongong High Performance Steering Committee Level: 300

1. © 2017, Amazon Web Services, Inc. or its Affiliates, All rights reserved.
Real-world High Performance & High Throughput
Computing on AWS
Adrian White, Manager, APAC Research & Technical Computing, AWS
Dr Matthew Berryman, Chair of University of Wollongong High Performance Steering Committee
August, 2017

2. HPC is a tool and it’s changing.

3. Tools should fit your workflows
Collect WorkflowsEvents Integrate Discover
Validate &
Share
Real-time
Batch
Amazon
Kinesis Amazon EMR
Streaming
Amazon S3
AWS Lambda
AWS Batch
Amazon S3 Amazon S3
AWS CLI &
SDKs
Amazon
EMR
Amazon
Redshift
Amazon
Athena
HPC cluster
Amazon API
Gateway

4. A cluster in the cloud is an ephemeral tool
Amazon S3
Source data IN
Data product OUT

5. Compute in the cloud is flexible
M4
General
purpose
Compute
optimized
C4
C3
Storage and I/O
optimized
I3
G2
GPU or FPGA
enabled
Memory
optimized
D2
M3
X1
P2
F1
R4
R3
C5
I2 HS1

6. Instances types within a family
Model vCPUs Memory (GiB) Networking
Performance
r4.xlarge 2 15.25 Up to 10 Gbps
r4.xlarge 4 30.5 Up to 10 Gbps
r4.2xlarge 8 61 Up to 10 Gbps
r4.4xlarge 16 122 Up to 10 Gbps
r4.8xlarge 32 244 10 Gbps
r4.16xlarge 64 488 20 Gbps
R4

7. Clusters in the cloud are fit for purpose
Amazon S3
R4
P2 P2 P2
P2 P2 P2 R4
C5
C5
C5 C5
C5 C5

8. GPU and FPGA instances
P2: GPU instance
• Up to 16 NVIDIA GK210 (8 X K80) GPUs in a single instance, with
peer-to-peer PCIe GPU interconnect
• Supporting a wide variety of use cases including deep learning,
HPC simulations, financial computing, and batch rendering
F1: FPGA instance
• Up to 8 Xilinx Virtex® UltraScale+™ VU9P FPGAs in a single
instance, with peer-to-peer PCIe and bidirectional ring
interconnects
• Designed for hardware-accelerated applications including financial
computing, genomics, accelerated search, and image processing
P2
F1

9. Clusters can scale and are elastic
W = 1, C =1 W = n, C = n W = 0, C ~ 0
t
c c
t
c
t

10. Data + consumers

11. 𝑉𝑎𝑙𝑢𝑒(𝑑𝑎𝑡𝑎) ∝
𝑚 𝑑1 . 𝑚(𝑑2)
𝑟2
“Data has gravity…”

12. Everyone consumes S3
Collect Share

13. Keep storage simple – S3 and POSIX cache
M M M
S S S S
Mgt
N.B. Data lifecycle is
required

14. HPC+HTC Tools

15. A few HPC and HTC tools on AWS
CfnCluster AWS Batch Amazon EMR+
CfnCluster is
provided by AWS to
quickly provision
configurable HPC
and HTC cluster
environments
Alces Flight is available
in the AWS Marketplace
and bundles 1000+
commonly used
scientific applications
https://aws.amazon.com
/marketplace/
AWS Batch provides
compute resources via
Docker containers
with user-definable
queues and an
optimised job
scheduler
Amazon EMR
provides a managed
Hadoop framework
supporting Apache
Spark, HBase, Presto,
and Flink on Amazon
EC2 and EC2 Spot

16. A closer look at Alces Flight
1000+ popular scientific applications
• Multiple versions, complete with libraries and
various compiler optimizations, ready to run
• Supports Docker and Singularity
• Slurm default scheduler (also PBS Pro, SGE
etc)
Available via the AWS Marketplace
http://alces-flight.com/ for more information

17. R4
…
OpenDroneMap on Alces Flight architecture
r4.8xlarge
32 vCPUs
244 GiB RAM
10 Gbps

18. R4
R4
…
OpenDroneMap on Alces Flight architecture

19. Demo: Push-button HPC + Aerial
imagery processing

20. So, how much did this cost?
On-demand / hr Spot / hr Running total
Login node
(r4.2xlarge)
$1.91 $1.91
Compute nodes
(r4.8xlarge x 4)
$8.52 $1.20
(85% saving)
$3.60
Shared storage
(1TB general purpose SSD
via NFS)
$0.17 $0.17
Data transfer + S3
(egress)
$0.07 $0.07
Processing time: 3 hours
GRAND TOTAL: $5.75

21. Future thoughts…

22. Evolving the compute paradigm
Physical Virtualization Containerization Serverless

23. “HPC” on Lambda?
def my_function(b):
x = np.random.normal(0, b, 1024)
A = np.random.normal(0, b, (1024, 1024))
return np.dot(A, x)
pwex = pywren.default_executor()
res = pwex.map(my_function, np.linspace(0.1, 100, 1000))
PyWren.io
PyWren lets you run your existing
python code at massive scale via
AWS Lambda
CSIRO have built GT-
Scan2 for CRISPR/Cas9
analysis on AWS Lambda

24. Before you go home…

25. Do two things!
+
Register and enroll in the
AWS Research Cloud Program
https://aws.amazon.com/rcp
Launch your own personal cluster
Using Alces Flight
http://alces-flight.com/community
1. 2.

26. More Information
• High Performance Computing on AWS
(whitepapers, customer examples, tech overviews)
• AWS Compute Blog
• Research & Technical Computing on AWS
• AWS Research Cloud Program
Demos (courtesy of AcrossTheCloud.net)
IoT button demo (GitHub)
Alces Flight ODM demo (GitHub)
HPC + HTC Tools
CfnCluster, AWS Batch, Alces Flight
Pywren: Terraflops and microservices

27. © 2017, Amazon Web Services, Inc. or its Affiliates, All rights reserved.
Thank you!
Adrian White, Manager, APAC Research & Technical Computing, AWS
Dr Matthew Berryman, Chair of University of Wollongong High Performance Steering Committee
August, 2017