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© 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
David Pellerin, Business Development Principal, ...
What to Expect from the Session
Overview of use cases for HPC in science, aerospace, automotive and
manufacturing, life sc...
What is HPC?
Use Cases
• High-energy physics simulations
• Weather and climate modeling and prediction
• Analysis of fluids, structures, and mate...
Scale Matters: for Big Data and Big Compute
Big Data
drives
Big Compute
in
Big Science
Big Compute on AWS for Big Science
HPC in Energy Management
Big Data meets Big Compute
"Fugro Roames has enabled Ergon Energy to
reduce the cost of vegetation management from
AU$100 ...
HGST applications for engineering:
 Molecular dynamics, CAD, CFD, EDA
 Collaboration tools for engineering
 Big data fo...
16M cell, polyhedral,
external aero case
Running on c4.8xlarge
instances
Demonstrates excellent
scalability for typical
CF...
Mapping HPC Use-Cases
Data Light
Minimal
requirements for
high performance
storage
Data Heavy
Benefits from
access to high...
Cluster HPC and Grid HPC on the Cloud
Cluster HPC
Tightly coupled,
latency sensitive
applications
Use larger EC2
compute i...
What Does This Mean for Simulations?
Expand the simulation domain
Run larger numbers of parallel, clustered HPC jobs
Performance Testing
for Common Applications
Weather Prediction
WRF Scaling and Performance on AWS
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4.5
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Scale...
ANSYS Mechanical FEA Performance
ENGINE BLOCK (V17cg-3) (PCG solver)
Static structural analysis of an engine block
without...
Performance for Fluid Dynamics on AWS
ANSYS Fluent
• AWS c4.8xlarge
• 140M cells
• F1 car CFD benchmark
http://www.ansys-b...
Test using larger, real-world examples
• Use large cases for testing: do not benchmark scalability
using only small exampl...
Choose a cell-to-core
ratio to optimize core
efficiency, to optimize
license costs, or to
achieve faster results
Higher pe...
OS version
• Use Amazon Linux or a version 3.10 or later Linux kernel
Processor states and affinity
• Use P-states to redu...
EC2 Instance Types
for HPC
Broad Set of Compute Instance Types
M4
General
purpose
Compute
optimized
C4
C3
Storage and I/O
optimized
I3
G2
GPU or FPGA...
Diving Deep: M4.16xlarge M4
CPU-Based Instances for HPC
Intel CPUs
• Up to 2.9 GHz, Turbo enabled up to 3.6 GHz
• Intel® Advanced Vector Extensions (I...
GPU and FPGA Instances
P2: GPU instance
• Up to 16 NVIDIA GK210 (8 X K80) GPUs in a single instance, with
peer-to-peer PCI...
P2 GPU Instances
• Up to 16 K80 GPUs in a single instance
• Including peer-to-peer PCIe GPU interconnect
• Supporting a wi...
F1 FPGA Instances
• Up to 8 Xilinx Virtex UltraScale Plus VU9p FPGAs in a single instance
with four high-speed DDR-4 per F...
Why FPGAs?
featuring
Genomic Big Data
Scale
• Population scale genomics
• Precision medicine for all
• Liquid biopsy cancer screenings
DNA data...
FPGAs for Genomics HPC
Highly Efficient
• Algorithms implemented in hardware
• Gate-level circuit design
• No instruction ...
www.edicogenome.com
Deploying HPC
on AWS
Traditional HPC Stack
Shared file storage
HPC cluster
License managers and cluster
head nodes with job schedulers
3D graph...
Migrating HPC to AWS
Shared File Storage
Cloud-based, scaling HPC cluster
on EC2
License managers and cluster
head nodes w...
Deploying HPC
on AWS (Legacy)
Deploying HPC
on AWS (Optimized)
Use different on-demand
HPC clusters for
different applications
or end-users
1. Users acc...
Automation Capabilities: CfnCluster
• CfnCluster simplifies
deployment of HPC in the
cloud, including integrating
with pop...
Amazon S3
Secure, durable,
highly-scalable object
storage. Fast access,
low cost.
For long-term durable
storage of data, i...
Secure Graphics and Collaboration
Cloud can be used for pre-and post processing as well as HPC
• Use GPUs in the cloud for...
1) Customer Managed Application Hosting
• Customer has account with AWS and manages virtual infrastructure
• Cloud used fo...
Example: ANSYS Enterprise Cloud
Example: Altair HyperWorks on AWS
Virtual screening at Novartis
• 10 million compounds screened
against a cancer target, in only 9 hours
• Approximately 87,...
● Customer:
● Reduced analysis time from 5.3 days to 12 hours
● Instantly scaled up to 48 cores
HPC Partner on AWS: Rescal...
HPC Partner on AWS: Alces Flight
www.alces-flight.com
Future Trends: Microservice-Based HPC
www.algorithmia.com
Next Steps
Visit aws.amazon.com/hpc
Additional sessions:
• CMP314 - Bringing Deep Learning to the Cloud with Amazon EC2
• ...
Thank you!
Remember to complete
your evaluations!
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AWS re:Invent 2016: High Performance Computing on AWS (CMP207)

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High performance computing in the cloud is enabling high scale compute- and graphics-intensive workloads across industries, ranging from aerospace, automotive, and manufacturing to life sciences, financial services, and energy. AWS provides application developers and end users with unprecedented computational power for massively parallel applications, in areas such as large-scale fluid and materials simulations, 3D content rendering, financial computing, and deep learning. This session provides an overview of HPC capabilities on AWS, describes the newest generations of accelerated computing instances (including P2), as well as highlighting customer and partner use-cases across industries.

Attendees learn about best practices for running HPC workflows in the cloud, including graphical pre- and post-processing, workflow automation, and optimization. Attendees also learn about new and emerging HPC use cases: in particular, deep learning training and inference, large-scale simulations, and high performance data analytics.

Published in: Technology

AWS re:Invent 2016: High Performance Computing on AWS (CMP207)

  1. 1. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved. David Pellerin, Business Development Principal, HPC November 30, 2016 High Performance Computing on AWS CMP207
  2. 2. What to Expect from the Session Overview of use cases for HPC in science, aerospace, automotive and manufacturing, life sciences, financial services, and energy. Overview of HPC capabilities on AWS, including: • The newest compute-optimized instances • P2 GPU instances • F1 FPGA instances Best practices for running traditional and new/emerging HPC workflows in the cloud, including graphical pre- and post-processing and workflow automation
  3. 3. What is HPC? Use Cases
  4. 4. • High-energy physics simulations • Weather and climate modeling and prediction • Analysis of fluids, structures, and materials • Thermal and electromagnetic simulations • Genomics, proteomics, and molecular dynamics • Seismic and reservoir simulations • 3D rendering and visualizations • Deep learning training and inference Cloud unlocks HPC for a broad range of use cases AWS for High Performance Computing…
  5. 5. Scale Matters: for Big Data and Big Compute
  6. 6. Big Data drives Big Compute in Big Science
  7. 7. Big Compute on AWS for Big Science
  8. 8. HPC in Energy Management
  9. 9. Big Data meets Big Compute "Fugro Roames has enabled Ergon Energy to reduce the cost of vegetation management from AU$100 million to AU$60 million per year.” - Josh Passenger, Technical Architect, Fugro Roames • Aircraft equipped with cameras, laser sensors • Repeated overflights of power networks • Captured data is used to render detailed 3D models of the power lines, and the environment • Analytics and simulations are run to generate actionable reports for directing post-disaster repair and prioritizing ongoing maintenance
  10. 10. HGST applications for engineering:  Molecular dynamics, CAD, CFD, EDA  Collaboration tools for engineering  Big data for manufacturing yield analysis HPC for Engineering Simulations Running drive-head simulations at scale: Millions of parallel parameter sweeps, running months of simulations in just hours Over 85,000 Intel cores running at peak, using Spot Instances
  11. 11. 16M cell, polyhedral, external aero case Running on c4.8xlarge instances Demonstrates excellent scalability for typical CFD models HPC for Aerospace
  12. 12. Mapping HPC Use-Cases Data Light Minimal requirements for high performance storage Data Heavy Benefits from access to high performance storage Fluid dynamics Weather forecasting Materials simulations Crash simulations Risk simulations Molecular modeling Contextual search Logistics simulations Animation and VFX Semiconductor verification Image processing/GIS Genomics Seismic processing Metagenomics Astrophysics Deep learning Clustered (Tightly Coupled) Distributed/Grid (Loosely Coupled)
  13. 13. Cluster HPC and Grid HPC on the Cloud Cluster HPC Tightly coupled, latency sensitive applications Use larger EC2 compute instances, placement groups, enhanced networking Grid HPC Loosely coupled, pleasingly parallel Use a variety of EC2 instances, multiple AZs, Spot, Auto Scaling, Amazon SQS Grids of Clusters Use a grid strategy on the cloud to run a group of parallel, individually clustered HPC jobs
  14. 14. What Does This Mean for Simulations? Expand the simulation domain Run larger numbers of parallel, clustered HPC jobs
  15. 15. Performance Testing for Common Applications
  16. 16. Weather Prediction WRF Scaling and Performance on AWS 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 0.0 500.0 1000.0 1500.0 2000.0 2500.0 0 500 1000 1500 2000 2500 3000 3500 4000 Time(S) Scale-Up Cores WRF 2.5 km CONUS Benchmark Scale-Up time
  17. 17. 0 20 40 60 80 100 120 140 160 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 0 50 100 150 200 250 300 350 ScaleUp Time(s) Cores c4.8xlarge Time c4.8xlarge Scaleup Structural Analysis AWG ERIF Test Case 2.1: Fan Blade-Off Rig Test, Generic Fan Rig Model
  18. 18. ANSYS Mechanical FEA Performance ENGINE BLOCK (V17cg-3) (PCG solver) Static structural analysis of an engine block without the internal components
  19. 19. Performance for Fluid Dynamics on AWS ANSYS Fluent • AWS c4.8xlarge • 140M cells • F1 car CFD benchmark http://www.ansys-blog.com/simulation-on-the-cloud/
  20. 20. Test using larger, real-world examples • Use large cases for testing: do not benchmark scalability using only small examples Domain decomposition • Choose number of cells per core for either per-core efficiency or for faster results Instance types • C4 or M4 are best choices today Network • Use a placement group • Enable enhanced networking Performance Considerations for HPC on AWS
  21. 21. Choose a cell-to-core ratio to optimize core efficiency, to optimize license costs, or to achieve faster results Higher per-core efficiency Faster results Domain Decomposition is Important
  22. 22. OS version • Use Amazon Linux or a version 3.10 or later Linux kernel Processor states and affinity • Use P-states to reduce processor variability • Use CPU affinity to pin threads to CPU cores MPI libraries • Intel MPI recommended Hyper-threading • Test with Hyper-threading on and off • Usually off is best, but not always Performance Considerations for HPC on AWS
  23. 23. EC2 Instance Types for HPC
  24. 24. Broad Set of Compute Instance Types 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
  25. 25. Diving Deep: M4.16xlarge M4
  26. 26. CPU-Based Instances for HPC Intel CPUs • Up to 2.9 GHz, Turbo enabled up to 3.6 GHz • Intel® Advanced Vector Extensions (Intel® AVX2) • Control over C-States, P-States, and Hyper-threading • C4, M4 are the most common instance types for HPC: • Up to 64 vCPUs (32 physical cores) • R3 and X1 for higher memory applications • Up to 128 vCPUs (64 physical cores), up to 2 TB RAM • Proprietary network delivering up to 20 Gbps
  27. 27. 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
  28. 28. P2 GPU Instances • Up to 16 K80 GPUs in a single instance • Including peer-to-peer PCIe GPU interconnect • Supporting a wide variety of use cases including deep learning, HPC simulations, and batch rendering P2 Instance Size GPUs GPU Peer to Peer vCPUs Memory (GiB) Network Bandwidth* p2.xlarge 1 - 4 61 1.25Gbps p2.8xlarge 8 Y 32 488 10Gbps p2.16xlarge 16 Y 64 732 20Gbps *In a placement group
  29. 29. F1 FPGA Instances • Up to 8 Xilinx Virtex UltraScale Plus VU9p FPGAs in a single instance with four high-speed DDR-4 per FPGA • Largest size includes high performance FPGA interconnects via PCIe Gen3 (FPGA Direct), and bidirectional ring (FPGA Link) • Designed for hardware-accelerated applications including financial computing, genomics, accelerated search, and image processing F1 Instance Size FPGAs FPGA Link FPGA Direct vCPUs Memory (GiB) NVMe Instance Storage Network Bandwidth* f1.2xlarge 1 - 8 122 1 x 480 5 Gbps f1.16xlarge 8 Y Y 64 976 4 x 960 30 Gbps *In a placement group
  30. 30. Why FPGAs? featuring
  31. 31. Genomic Big Data Scale • Population scale genomics • Precision medicine for all • Liquid biopsy cancer screenings DNA data doubles every 7 months – CPU speeds double every 2 years Size • Each person’s genome is ~100 GB • Computationally intensive analysis • Multiple copies stored forever DNA
  32. 32. FPGAs for Genomics HPC Highly Efficient • Algorithms implemented in hardware • Gate-level circuit design • No instruction set overhead Massively Parallel • Massively parallel circuits • Multiple compute engines • Rapid FPGA reconfigurability FPGA Speeds analysis of whole human genomes from hours to minutes Unprecedented low cost for compute and compressed storage
  33. 33. www.edicogenome.com
  34. 34. Deploying HPC on AWS
  35. 35. Traditional HPC Stack Shared file storage HPC cluster License managers and cluster head nodes with job schedulers 3D graphics remote desktop servers Remote graphics workstations Storage cache Remote sites Remote backup
  36. 36. Migrating HPC to AWS Shared File Storage Cloud-based, scaling HPC cluster on EC2 License managers and cluster head nodes with job schedulers 3D graphics virtual workstation AWS Direct Connect On-Premises IT Resources Thin or Zero Client - No local data - Storage CacheAmazon S3 and Amazon Glacier
  37. 37. Deploying HPC on AWS (Legacy)
  38. 38. Deploying HPC on AWS (Optimized) Use different on-demand HPC clusters for different applications or end-users 1. Users access resources via secure VPN tunnel 2. Cloud desktops are GPU-enabled for graphics performance 3. Hardened and monitored proxy server used for all access 4. Optional: AWS CodeCommit used for source code repo 5. Continuous Integration server used to manage builds 6. Simple Queueing Service used for queue-based job submission 7. Application-specific compute nodes automatically scaled based on demand 8. License server can be on-premises, or in cloud with results and logs pushed to S3 9. Coverage tracking system notified and updated as jobs complete
  39. 39. Automation Capabilities: CfnCluster • CfnCluster simplifies deployment of HPC in the cloud, including integrating with popular HPC schedulers
  40. 40. Amazon S3 Secure, durable, highly-scalable object storage. Fast access, low cost. For long-term durable storage of data, in a readily accessible get/put access format. Primary durable and scalable storage for HPC data Amazon Glacier Secure, durable, long term, highly cost- effective object storage. For long-term storage and archival of data that is infrequently accessed. Use for long-term, lower-cost archival of HPC data EC2+EBS Create a single-AZ shared file system using EC2 and EBS, with third-party or open source software (e.g., Intel Lustre). For near-line storage of files optimized for high I/O performance. Use for high-IOPs, temporary working storage AWS Storage Options for HPC Workloads EFS Highly available, multi-AZ, fully managed network- attached elastic file system. For near-line, highly- available storage of files in a traditional NFS format (NFSv4). Use for read-often, temporary working storage
  41. 41. Secure Graphics and Collaboration Cloud can be used for pre-and post processing as well as HPC • Use GPUs in the cloud for remote rendering and remote desktops Cloud is more secure for collaboration • Encrypt the data in flight and at rest • Manage your own keys and credentials • Deliver pixels to your collaborators, not the actual data
  42. 42. 1) Customer Managed Application Hosting • Customer has account with AWS and manages virtual infrastructure • Cloud used for batch jobs via cluster management software • Customer can also remote log in and collaborate using GPU instances • Customer maintains traditional software vendor relationships • Software vendor optionally offers license flexibility for scalable computing 2) Software Vendor Managed Application Hosting • SaaS or hybrid model for managed engineering apps in the cloud • Customer pays software vendor for cloud-hosted services • Customer does not need to manage virtual infrastructure Either method of software delivery is supported on AWS, and the right method will depend on customer requirements – for security and governance, ease of deployment, etc. Options for Software Licensing
  43. 43. Example: ANSYS Enterprise Cloud
  44. 44. Example: Altair HyperWorks on AWS
  45. 45. Virtual screening at Novartis • 10 million compounds screened against a cancer target, in only 9 hours • Approximately 87,000 compute cores at peak HPC Partner on AWS: Cycle Computing Engineering simulations at HGST: • Millions of parameter sweeps, running months of simulations in just hours • Over 85,000 Intel cores running at peak, using Spot Instances www.cyclecomputing.com
  46. 46. ● Customer: ● Reduced analysis time from 5.3 days to 12 hours ● Instantly scaled up to 48 cores HPC Partner on AWS: Rescale APN Advanced Partner Rescale’s cloud HPC platform • Offers native integration to over 180+ simulation and machine learning applications in a SaaS environment • Automation of systems tools and services enables seamless deployment of AWS • JL & Associates used Rescale on AWS to utilize multiphase CFD analysis for modeling boiling oil (C12H26) • The team was able to achieve their goal of steady state convergence which required 23k Iterations @ ~20 sec/It www.rescale.com
  47. 47. HPC Partner on AWS: Alces Flight www.alces-flight.com
  48. 48. Future Trends: Microservice-Based HPC www.algorithmia.com
  49. 49. Next Steps Visit aws.amazon.com/hpc Additional sessions: • CMP314 - Bringing Deep Learning to the Cloud with Amazon EC2 • CMP317 – Deep Learning, 3D Content Rendering, and Massively Parallel, Compute-Intensive Workloads in the Cloud • CMP318 – Building HPC Clusters as Code • CMP320 – Delivering Graphical Applications on AWS
  50. 50. Thank you!
  51. 51. Remember to complete your evaluations!

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