(cmp202) engineering simulation and analysis in the cloud

© 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

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

Design & Engineering in Manufacturing

Data & Process (PLM)

Conceptual

Design

Engineering Design (CAD)

Simulation & Analysis

(CAE)

Tooling Design (CAM)

Production

Engineering / Design Simulation (CAE)

Anatomy of a CAE solution

Client Server

Data

Identity /

Access

Job

Management

Mapping HPC ApplicationsTightly Coupled (MPI/HPC)

Loosely Coupled (Grid/HTC)

Data-IntensiveRequires high-IOPS

storage, or has very

large datasets

Data-LightLess 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)

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

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

“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

ANSYS Enterprise Cloud

Key Engineering Challenges

• HPC compute

• Interactive graphics

• Data management

• Solution deployment

Scale-out HPC

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

c4.largeInstance family

Instance generation

Instance size

Amazon EC2 Instances: Types and Sizes

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

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

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

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

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

Parallel Solver Performance

Solve real problems…

…and solve as many

designs as you like.

Interactive Graphics

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

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

Large Memory or GPU Instance? Why Not Both?

Technical User

Thin viewer

DCV protocol

over

HTTP(S)

DCV

Proxy

R3

G2

Op

en

GL

Gra

ph

ics

off

loadin

g

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.

Data Management

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

Solution Deployment

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

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

Remember to complete

your evaluations!

Thank you!

tambe@amazon.com