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Scientific Computing on Amazon Web ServicesJamie Kinney, AWS Scientific Computing

jkinney@amazon.com

AWS Scientific Computing (SciCo)

Scientific Computing Initiatives

Y0L0!

Why does Amazon care about Scientific Computing?• In order to meaningfully change our world for the better by accelerating

the pace of scientific discovery

• It is a great application of AWS with a broad customer base

• The scientific community helps us innovate on behalf of all customers– Streaming data processing & analytics– Exabyte scale data management solutions and exaflop scale compute– Collaborative research tools and techniques– New AWS regions– Significant advances in low-power compute, storage and data centers– Efficiencies which will lower our costs and therefore pricing for all customers

Why Did We Create SciCo?

In order to make it easy to find, discover and use AWS for scientific computing at any scale. Specifically, SciCo helps AWS:

• More effectively support global “Big Science” Collaborations• Develop a solution-centric focus for engaging with the global scientific and

engineering communities• Accelerate the development of a scientific computing ecosystem on AWS• Educate and Evangelize our role in Scientific Computing

How is AWS Used for Scientific Computing?

• High Performance Computing (HPC) for Engineering and Simulation

• High Throughput Computing (HTC) for Data-Intensive Analytics• Collaborative Research Environments• Hybrid Supercomputing centers• Citizen Science• Science-as-a-Service

Why do researchers love using AWS?

Time to ScienceAccess research

infrastructure in minutes

Low CostPay-as-you-go pricing

ElasticEasily add or remove capacity

Globally AccessibleEasily Collaborate with

researchers around the world

SecureA collection of tools to

protect data and privacy

ScalableAccess to effectively

limitless capacity

Research Grants

AWS provides free usage credits to help researchers:• Teach advanced courses• Explore new projects• Create resources for the

scientific community

aws.amazon.com/grants

Amazon Public Data Sets

AWS hosts “gold standard” reference data at our expense in order to catalyze rapid innovation and increased AWS adoption

A few examples:

• 1,000 Genomes ~250 TB

• Cancer Genomics Data Sets ~2-6 PB

• Astronomy Data and APIs 1PB+

• Common Crawl

• OpenStreetMap

• Census Data

Public Data Sets

Peering with all global research networks

Image courtesy John Hover - Brookhaven National Lab

Restricted-access genomics on AWS

aws.amazon.com/genomics

Curiosity Remote Control Overview

HPC on AWS

Characterizing HPC

Tightly Coupled

Loosely Coupled

Embarrassingly

parallel

Elastic

Batch workloads

Interconnected jobs

Network sensitivity

Job specific

algorithms

Characterizing HPC

Tightly Coupled

Loosely Coupled

Supporting

Services

Embarrassingly

parallel

Elastic

Batch workloads

Data management

Task distribution

Workflow

management

Interconnected jobs

Network sensitivity

Job specific

algorithms

Characterizing HPC

Tightly Coupled

Loosely Coupled

Supporting

Services

Embarrassingly

parallel

Elastic

Batch workloads

Data management

Task distribution

Workflow

management

Interconnected jobs

Network sensitivity

Job specific

algorithms

High-throughput computing

S3 Amazon EMRVery high,non-blocking, parallel bandwidth

2. Start a cluster(Hadoop, SGE,

custom)1. Put data in S3

3. Get the results

Easily scale to more computational nodes

Computational compound analysisSolar panel material Estimated serial computation time 264 years

156,314 core cluster across 8 regions1.21 petaFLOPS (Rpeak)

Simulated 205,000 materials18 hours for $33,000 16¢ per molecule

http://news.cnet.com/8301-1001_3-57611919-92/supercomputing-simulation-employs-156000-amazon-processor-cores/

Use Spot instances to save $$$

On

On-demand

Reserved capacity

100%

Capacity Over Time

AWS Spot MarketAchieving economies of scale

Spot

0%

How do Spot instances work?

2 minute warning

Spot price market examples

• r3.2xlarge– 8 vCPU– 61GB RAM– 1 x 160GB D– $0.70 per hour

* Prices on April 17, 2015

Spot price market examples

• r3.4xlarge– 16 vCPU– 122GB RAM– 1 x 320GB SSD– $1.40 per hour

* Prices on April 17, 2015

Spot price market examples

• c3.8xlarge– 32 vCPU– 60GB RAM– 2 x 320 SSD– $1.68 per hour

* Prices on April 17, 2015

Spot friendly workloads• All Spot applications should be…

– Time-flexible: You can’t expect Spot to always be available, so they can wait until it is, or use OD/RI instances

– Fault-tolerant: the application can gracefully handle Spot interruptions– (Because Spot may not be available and Spot Instances may be interrupted)

• Great Spot applications are…– Distributed: the application has jobs that can be spread out across many

Spot Instances, instance types, AZs, even regions– Scalable: the application can pile on more Spot Instances in parallel to get

its job(s) done faster– (Access up to 10,000s of Spot Instances worldwide)

Spot friendly architectures

• Fault tolerant– Worker resources are ephemeral

• De-centralized workflows– Worker resources can run in multiple Availability Zones

• Stateless applications– Worker resources grab work items and data from resilient data

stores– Amazon SQS, Amazon SWF, Amazon S3

Using Spot effectively – normalize application requirements

• CPU Generation (Intel AVX2.0 in Haswell)• Memory/core• Networking

Using Spot effectively – bidding strategies

• You only pay what current Market price• But, bid what you are willing to pay

Bid only what you are willing to pay.

(by default, bid limited to 4 * On Demand Price)

Frontend Applicationson On-Demand/Reserved Instances

+

Backend Applications*on Spot Instances

* e.g., image segmentation, genomic alignment

Use On-Demand & Spot

Amazon EC2 Auto Scaling Spotaws autoscale create-launch-configuration --launch-configuration-name spotlc-5cents --image-id ami-e565ba8c --instance-type d2.2xlarge --spot-price “0.25”

aws autoscale create-auto-scaling-group --auto-scaling-group-name spotasg --launch-configuration spotlc-5cents --availability-zones “us-east-1a,us-east-1b” --max-size 16 --min-size 1 --desiredcapacity 3

http://aws.amazon.com/cli/

Leverage Spot instances in workflows1 days worth of effort

resulted in 50% savings in cost

Harvard Medical SchoolThe Laboratory of Personal Medicine

Run EC2 clusters to analyze entire genomes

“The AWS solution is stable, robust, flexible, and low cost. It has everything to recommend it.”

Dr. Peter Tonellato, LPM, Center for Biomedical Informatics, Harvard Medical School

http://aws.amazon.com/solutions/case-studies/harvard/

https://cosmos.hms.harvard.edu/

Parallel clusters can work on the same data

Amazon EC2

Downstream analysis on local compute or

EC2

Launch in VPC for secure computing

Science Ecosystem

StorageDatabaseCompute

Partner Ecosystem

Characterizing HPC

Tightly Coupled

Loosely Coupled

Supporting

Services

Embarrassingly

parallel

Elastic

Batch workloads

Data management

Task distribution

Workflow

management

Interconnected jobs

Network sensitivity

Job specific

algorithms

Cluster instances deployed in a ‘Placement

Group’ enjoy low latency, full bisection 10

Gbps bandwidth

10Gbps

Network placement groups

Enhanced Networking: SR-IOVA standardized way for a single I/O device to present itself as multiple separate device

http://glennklockwood.blogspot.com/2013/12/high-performance-virtualization-sr-iov.html

OpenFOAM® Computational Fluid Dynamics software simulating the environmental conditions at Cyclopic Energy’s wind power sites

Case Study

Cyclopic EnergyCluster Compute

Two months worth of simulations in a two-day period

“AWS makes it possible for us to deliver state-of-the-art technologies to clients within timeframes that allow us to be dynamic, without having to make large investments in physical hardware.”

Intel Xeon E5-2670 (Sandy Bridge)

15GB or 60GB RAM

1 NVIDIA Grid k520 GPU 1,536 Cores4GB Mem

GPU instance sizeGPU Optimized

Name GPU vCPUMemory

(GiB)Networ

kSSD

g2.2xlarge

1 8 15 High 1 x 60

g2.8xlarge

4 32 60 10 Gb 2 x 120

Massive parallel clusters running in

GPUs

NVIDIA Tesla cards in specialized

instance types

CUDA & OpenCL

Case Study

National Taiwan University50 x cg1.4xlarge instance types

100 nvidia Tesla M2050

“Our purpose is to break the record of solving the shortest vector problem (SVP) in Euclidean lattices…the vectors we found are considered the hardest SVP anyone has solved so far.”

Prof. Chen-Mou Cheng, the Principal Investigator of Fast Crypto Lab

$2,300 for using 100 Tesla M2050 for ten hours

Characterizing HPC

Tightly Coupled

Loosely Coupled

Supporting

Services

Embarrassingly

parallel

Elastic

Batch workloads

Data management

Task distribution

Workflow

management

Interconnected jobs

Network sensitivity

Job specific

algorithms

Middleware Services

Data managementFully managed SQL, NoSQL and object storage

1

Relational Database

Service

Fully managed database

(MySQL, Oracle, MSSQL)

DynamoDB

NoSQL, Schemaless,

Provisioned throughput

database

S3

Object datastore up to

5TB per object

99.999999999%

durability

Collection CollaborationComputation

Moving computation nearer the

data“Big Data” changes dynamic of computation and data

sharing

Direct Connect

Import/Export

S3

DynamoDB

EC2

GPUs

Elastic Map Reduce

CloudFormation

Simple Workflow

S3

Middleware Services

Feeding workloadsUsing highly available Simple Queue

Service to feed EC2 nodes

2

Amazon SQS

Processing

task/processing

trigger

Processing results

Middleware Services

Coordinating workloads & task clustersHandle long running processes across many nodes and task

steps with Simple Workflow

3

Task A

Task B

(Auto-

scaling)Task C

2

3

1

Other Stuff

• A centralized repository of public datasets

• Seamless integration with cloud based applications

• No charge to the community• Tell us what else you’d like for

inclusion …

AWS Public Data Sets1000 Genomes Project

Genome in A Bottle

Landsat 8

NASA NEX: Earth science data sets

The Cannabis Sativa Genome

US Census Data: US demographic data from 1980, 1990, and 2000 US Censuses

Freebase Data Dump: A data dump of all the current facts and assertions in the Freebase system, an open database covering millions of topics

Google Books n-grams

SDKs

Java Python PHP .NET Ruby nodeJS

iOS Android AWS Toolkit for Visual

Studio

AWS Toolkit for Eclipse

Tools for Windows

PowerShell

CLI

AWS Services are a few clicks away…

https://console.aws.amazon.com

AWS Research Grantshttp://aws.amazon.com/grants

A Simple Form