Time to Science, Time to Results: Accelerating Research with AWS - AWS Symposium 2014 - Washington...

AWS Government, Education, and Nonprofits Symposium Washington, DC | June 24, 2014 - June 26, 2014


Accelerating Research with AWS

Steve [email protected]

Jamie [email protected]

Angel [email protected]


Why?• “Work hard, have fun, make history”• Accelerate the pace of scientific discovery

What?• Motivations, Theory, and Practice


AWS Research Grants• Apply for credits to teach advanced courses,

tackle research endeavors, and explore new projects

• Bootstrap projects that previously would have required expensive up-front and ongoing investments in infrastructure


:

http://aws.amazon.com/solutions/case-studies/university-of-california-berkeley-amp-lab-carat-project/


Some more examples• MIT, Mark Pearrow, McGovern Institute

– Genetic and computational analysis, electrophysiological recordings, and non-invasive brain imaging

• University of Illinois Urbana-Champaign, Indranil Gupta, Computer Science

– Research issues in loosely federated clouds

• Singapore Management University, Ming Jiang – New techniques in malware analysis

• Technion, Israel Institute of Technology, Alex Zlotnik

– Systems for efficient execution of scientific workloads

• University of Maryland, Michael Schatz, Center for Bioinformatics and Computational Biology

– Assembly of large genomes using cloud computing

• ETH Zurich, Till Quack, Computer Vision Lab – Large scale annotation of photo collections

• University of Pennsylvania, Zachary Ives, Computer and Information Science Department

– Orchestra, collaborative data sharing system on the cloud

• Monash University, Blair Bethwaite, eScience and Grid Engineering Laboratory

– Mixing grids and clouds for high throughput science

• Harvard University, Vinothan N. Manoharan, SEAS, Department of Physics

– Exploring the physics of self-organization with digital holographic microscopy


Take home messageAWS Research Grants are a great way to bootstrap a project, or experiment on AWS

http://aws.amazon.com/grants


Scientific Computing Initiatives

Y0L0!


UCSF, UCSC, UCB

BGI

University of Cape Town

UT/MD Anderso

n

Seven Bridges Genomic

s

Caltech

Monash Universi

ty

Sanger Institut

eWellcome Trust

Fred Hutchinson

Cancer Research Center &

Sage Bionetworkks

BroadInstitute

OICR

U. Chicago

Plus hundreds of other sites around the world forCo-Is and ColleaguesCancer

Research UK

OHSU

RIKENIndian

Society of Human

Genetics

Global Alliance for Genomics & Health


1+ Million Cancer Genome Data

Warehouse


Enable collaboration• Easily and securely share data and

applications across institutions• Publish preconfigured resources


Data to the compute


Compute to the data


Download and Copy

S3Amazon RDS


Amazon RDS

Access in the Cloud

S3

RDS

RDS

RDS


Compute in the Cloud

S3

Amazon RDS


Baylor College of MedicineA platform built by Baylor College of Medicine Human Genome Sequencing Center and DNANexus using the Mercury Pipeline for the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium

Supports 300+ researchers around the world

Analyzed the genomes of over 14,000 individuals, encompassing 3,751 whole genomes and 10,940 whole exomes (~1PB of data)

Used 3.3 million core hours over 4 weeks to complete the job 5.7x faster than what could have been accomplished on-premise

The outcomes?

1. Easier collaboration

2. Faster time to science

3. Cost-effective: On-premise was prohibitively expensive

4. No longer constrained by on-premise capacity

5. Scientists focusing on Science as opposed to infrastructure


• 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 us to

host …

AWS Public Data Sets1000 Genomes Project

Ensembl, GenBank, UniGene, PubChem

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


Technical computing: Why AWS?

The IT infrastructure needed for technical computing is:

Large, complex, expensive

Poorly utilized due to project cycles

Rapidly obsolete due to technology advances

Big simulations can require days or weeks per iteration

“Time in the queue” is a growing problem in larger firms

Result? Engineering innovation is slowed


Big JOB to do …


… with little resources to do it.


Use a large shared resource …


?

… but there is a queue.


The hidden cost of queues

• HPC users seek fastest possible time-to-results and must compete for scarce cluster resources

• IT support team seeks highest possibility utilization of expensive cluster resources

• Result:

• The job queue becomes the buffer for managing IT capacity

• Time needed to complete simulations is too long and hard to predict

?


Properly size your clusters …


… from small …


… to large …


… and lots of them!


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/


Time: +00h

<10 cores


Time: +24h>1500 cores


Time: +72h

<10 cores


AWS value for HPC• Security: Deploy applications and store data in a secure, highly

configurable VPC environment

• Agility: Deploy the right infrastructure for each technical computing job, at the right time

• Scalability: Add and subtract servers in minutes to optimize time-to-results

• Cost Savings: Pay only for what you use, don’t pay for idle or outdated servers


Experimentoften

Fail quickly,at low cost

MoreInnovation


HPC Partners and Apps


Kyushu UniversitySupport seasonal demand for engineering and science computational resources.


Downstream Analysis

Compute Analytics ToolsDatabasesStorage


Questions

?


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


Elastic Map Reduce

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


Use Spot instances to save $$$



Amazon EC2


Launch in VPC for secure computing

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