(CMP202) Engineering Simulation and Analysis in the Cloud
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"Building great products, ones that are aesthetically appealing as well as functionally sound, requires cutting-edge design and engineering. Given the high cost of physical testing prototypes, engineering organizations are turning to simulation and analysis using digital models, but compute requirements for these have traditionally required expensive on-premises infrastructure. But now, engineering organizations can use high-performance computing services from AWS and solutions from AWS technology partners to innovate at scale globally, with no up-front capital infrastructure investment. In this session, AWS Partner Ansys shares how they help customers of all sizes design and engineer better products through digital simulation and analysis using HPC on AWS."
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(CMP202) Engineering Simulation and Analysis in the Cloud
- 1. © 2015, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Tosh Tambe – AWS Strategic Alliances – Design / Engineering & HPC Judd Kaiser – Program Mgr., Cloud Computing, ANSYS, Inc. October 2015 CMP202 Engineering Simulation and Analysis in the Cloud
- 2. What to Expect from the Session • Overview of HPC usage in Design / Engineering Simulation & Analysis (CAE) • Understanding challenges of HPC users in CAE • Customer Example: How ANSYS re-engineered their CAE HPC solution for cloud deployment
- 3. Design & Engineering in Manufacturing Data & Process (PLM) Conceptual Design Engineering Design (CAD) Simulation & Analysis (CAE) Tooling Design (CAM) Production
- 4. Engineering / Design Simulation (CAE)
- 5. Anatomy of a CAE solution Client Server Data Identity / Access Job Management
- 6. Mapping HPC Applications Tightly Coupled (MPI/HPC) Loosely Coupled (Grid/HTC) Data-Intensive Requires high-IOPS storage, or has very large datasets Data-Light Less dependence on high-IOPS, with smaller datasets Financial simulations Molecular modeling Contextual search Alt-coin mining Animation rendering Semiconductor verification Image processing / GIS Genomics Seismic processing High energy physics Metagenomics Human Brain Project Fluid dynamics Weather forecasting Materials simulations Crash simulations Grid Computing (“Pleasingly parallel”) Grid with IO Cluster Computing Cluster with IO (Data-intensive HPC)
- 7. Design Exploration with CAE Toyota Motor Corporation’s TPS process Source: Durward K. Sobek II, Allen C. Ward and Jeffrey K. Liker – sloanreview.mit.edu
- 8. Scaling Out Simulation: Platform Strategy Multiphysics Simulation Systems Engineering Robust Design Simulation Democratization Simulation Trends Desktop Platform Enterprise Platform • HPC • Data Mgmt. • Process Mgmt. ANSYS EKM
- 9. “Friends don’t let friends build data centers.” 04/14/2014 – Charles Phillips, CEO, Infor HPC on the cloud Cloud-Based Strategies for Delivery • Immediate access (training/demo) • Short term use • Burst to the cloud for HPC • Cloud for short term projects • End-to-end simulation Application streaming Software as a service
- 11. Key Engineering Challenges • HPC compute • Interactive graphics • Data management • Solution deployment
- 12. Scale-out HPC
- 13. Amazon EC2 instances: Families and Generations General-purpose: M3 , M4, T2 Compute-optimized: CC2, C3, C4 Memory-optimized: M2, CR1, R3 Dense-storage: HS1, D2 I/O-optimized: HI1, I2 GPU: CG1, G2 Micro: T1, T2
- 14. c4.large Instance family Instance generation Instance size Amazon EC2 Instances: Types and Sizes
- 15. Performance factors: CPU Intel Xeon E5-2670 (Sandy Bridge) CPUs • Available on M3, CC2, CR1, and G2 instance types Intel Xeon E5-2680 v2 (Ivy Bridge) CPUs • Available on C3, R3, and I2 instance types • 2.8 GHz in C3, Turbo enabled up to 3.6 GHz • Supports Enhanced Advanced Vector Extensions (AVX) instructions Intel Xeon E5-2666 v3 (Haswell – AVX2) CPUs • Available on C4, D2, and M4 instance types • 2.9 GHz in C4, Turbo enabled up to 3.5 GHz (with Intel Turbo Boost) • Supports AVX2 instructions
- 16. C4: CPU-Optimized Haswell Instance Type • 2.9 GHz Intel Xeon E5-2666v3 (Haswell) CPU • Turbo enabled to 3.5 GHz • Multiple instances sizes with 2, 4, 8, 16, 36 vCPUs • From 3.75GiB to 60GiB RAM • Optimized for use with Elastic Block Storage (SSD) for higher IOPS
- 17. Performance Factors: Networks AWS proprietary 10 Gb networking • Highest performance in .8xlarge instance sizes • Full bi-section bandwidth in placement groups Enhanced networking • Available on D2, C3, C4, M4, R3, I2 • Over 1M PPS performance, reduced instance- to-instance latencies, consistent performance
- 18. ANSYS Enterprise Cloud: HPC on AWS Auto-scaling HPC provisions resources on-demand, using machine configurations optimized for specific workloads. • Scale on demand • Match compute instances to workloads • Optimize steady state (reserved) and on-demand AWS spend
- 19. CycleCloud: Managing WorkLoad Lifecycles Teardown • Reporting • Usage tracking • Auditing Provisioning • On-demand • Spot pricing • Multi-provider Configuration • Chef (Puppet) • Cluster-Init Monitoring • Auto-scaling • Job tracking • Error handling
- 20. Parallel Solver Performance Solve real problems… …and solve as many designs as you like.
- 22. Performance Factors: Accelerators NVIDIA GPUs! • For computing and for remote graphics • In EC2 CG1 and G2 instances • GPU accelerators augment CPU- based computing by offloading specialized processing • Performance gains depend on application-level support
- 23. ANSYS Enterprise Cloud: Graphics Remote rendering delivers 3D graphics performance and large memory, providing a high-end workstation experience in the cloud. • Rendering on Linux g2.2xlarge • r3 application server running Windows, up to 244 GB of RAM
- 24. Large Memory or GPU Instance? Why Not Both? Technical User Thin viewer DCV protocol over HTTP(S) DCV Proxy R3 G2 OpenGL Graphics offloading HW Acceleration and Compression Large memory models G2 multiplexing Leverage latest NVIDIA GRID API • 3 to 10 times less bandwidth • Lower latency in pixel capture Optimized network usage • Dynamic image compression, with quality boost for still images Memory rightsizing for each problem / model size 24+ FPS with the most demanding use cases • Fully interactive collaboration sessions Workstation class responsiveness even across continental links Collab.
- 25. Data Management
- 26. Data Management • Amazon RDS for database • Takes are of data management tasks such as snapshots and back-ups • Amazon EBS for work-in-progress data • In the cluster file system – match capacity to customer requirements • Using SSD – good performance • Amazon S3 for archive data • Low cost, high redundancy • High quality back-up solution • Multi-part upload allows rapid data transfer
- 28. Solution Deployment • We deploy a full solution in a dedicated customer account • Set of CloudFormation templates • Use CloudFormation parameters and Lambda features to customize the deployment in multiple regions • Also use Cycle to deploy elastic infrastructure using Chef • Full deployment in less than three hours, as compared with months for on premise deployment
- 29. Summary • Workloads such as CAE benefit from HPC on AWS • Several AWS instance families ideal for HPC • Pick the optimal ones • HPC benefits from AWS networking services • Placement groups, enhanced networking • Focus on good user experience • Data and user management are key • Make deployment easy for users • AWS CloudFormation and AWS Lambda • Leverage the AWS partner ecosystem for technology building blocks
- 30. Remember to complete your evaluations!