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Scaling Science
Matt Wood
November 21, 2012
Science
3
260
Scaling Science
Introducing five principles for reproducibility.
Matt Wood
November 21, 2012
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Transcript
Scaling Science matthew@amazon.com Dr. Matt Wood
Hello
Science
Beautiful, unique.
Impossible to re-create
Snowflake Science
Reproducibility
Reproducibility scales science
Reproduce. Reuse. Remix.
Value++
None
How do we get from here to there? 5PRINCIPLES REPRODUCIBILITY
OF
1. Data has Gravity 5 PRINCIPLES REPRODUCIBILITY OF
Increasingly large data collections
1000 Genomes Project: 200Tb
Challenging to obtain and manage
Expensive to experiment
Large barrier to reproducibility
Data size will increase
Data integration will increase
Data dependencies will increase
Move data to the users
Move data to the users X
Move tools to the data
Place data where it can consumed by tools
Place tools where they can access data
None
None
None
Canonical source
None
More data, more users, more uses, more locations
Cost
Force multiplier
Cost
Complexity
Cost and complexity kill reproducibility
Utility computing
Availability
Pay-as-you-go
Flexibility
Performance
CPU + IO
Intel Xeon E5 NVIDIA Tesla GPUs
240 TFLOPS
90 - 120k IOPS on SSDs
Performance through productivity
Cost
On-demand access
Reserved capacity
100% Reserved capacity
100% Reserved capacity On-demand
100% Reserved capacity On-demand
Spot instances
Utility computing enhanced reproducibility
None
2. Ease of use is a pre-requisite 5 PRINCIPLES REPRODUCIBILITY
OF
http://headrush.typepad.com/creating_passionate_users/2005/10/getting_users_p.html
Help overcome the suck threshold
Easy to embrace and extend
Choose the right abstraction for the user
$ ec2-run-instances
$ starcluster start
None
Package and automate
Package and automate Amazon machine images, VM import
Package and automate Amazon machine images, VM import Deployment scripts,
CloudFormation, Chef, Puppet
Expert-as-a-service
None
None
1000 Genomes Cloud BioLinux
None
Your HiSeq data Illumina BaseSpace
Architectural freedom
Freedom of abstraction
3. Reuse is as important as reproduction 5 PRINCIPLES REPRODUCIBILITY
OF
Seven Deadly sins of Bioinformatics: http://www.slideshare.net/dullhunk/the-seven-deadly-sins-of-bioinformatics
Seven Deadly sins of Bioinformatics: http://www.slideshare.net/dullhunk/the-seven-deadly-sins-of-bioinformatics
Infonauts are hackers
They have their own way of working
The ‘Big Red Button’
Fire and forget reproduction is a good first step, but
limits longer term value.
Monolithic, one-stop-shop
Work well for intended purpose
Challenging to install, dependency heavy
Di cult to grok
Inflexible
Infonauts are hackers: embrace it.
Small things. Loosely coupled.
Easier to grok
Easier to reuse
Easier to integrate
Lower barrier to entry
Scale out
Build for reuse. Be remix friendly. Maximize value.
4. Build for collaboration 5 PRINCIPLES REPRODUCIBILITY OF
Workflows are memes
Reproduction is just the first step
Bill of materials: code, data, configuration, infrastructure
Full definition for reproduction
Utility computing provides a playground for bioinformatics
Code + AMI + custom datasets + public datasets +
databases + compute + result data
Code + AMI + custom datasets + public datasets +
databases + compute + result data
Code + AMI + custom datasets + public datasets +
databases + compute + result data
Code + AMI + custom datasets + public datasets +
databases + compute + result data
Package, automate, contribute.
Utility platform provides scale for production runs
Drug discovery on 50k cores: Less than $1000
5. Provenance is a first class object 5 PRINCIPLES REPRODUCIBILITY
OF
Versioning becomes really important
Especially in an active community
Doubly so with loosely coupled tools
Provenance metadata is a first class entity
Distributed provenance
1. Data has gravity 2. Ease of use is a
pre-requisite 3. Reuse is as important as reproduction 4. Build for collaboration 5. Provenance is a first class object 5PRINCIPLES REPRODUCIBILITY OF
None
Thank you aws.amazon.com @mza matthew@amazon.com