Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Scaling Science
Search
Matt Wood
November 21, 2012
Science
3
470
Scaling Science
Introducing five principles for reproducibility.
Matt Wood
November 21, 2012
Tweet
Share
More Decks by Matt Wood
See All by Matt Wood
Field Notes from Expeditions in the Cloud
mza
2
370
A Platform for Big Data
mza
6
720
The Data Lifecycle
mza
5
480
Provision Throughput Like a Boss
mza
0
420
Impact of Cloud Computing: Life Sciences
mza
2
830
Latency's Worst Nightmare: Performance Tuning Tips and Tricks
mza
4
1.1k
Under the Covers of DynamoDB
mza
4
1k
From Analytics to Intelligence: Amazon Redshift
mza
9
970
High Performance Web Applications
mza
6
610
Other Decks in Science
See All in Science
局所保存性・相似変換対称性を満たす機械学習モデルによる数値流体力学
yellowshippo
1
200
白金鉱業Meetup Vol.15 DMLによる条件付処置効果の推定_sotaroIZUMI_20240919
brainpadpr
2
730
Pericarditis Comic
camkdraws
0
1.5k
The thin line between reconstruction, classification, and hallucination in brain decoding
ykamit
1
1.3k
butterfly_effect/butterfly_effect_in-house
florets1
1
160
大規模言語モデルの論理構造の把握能力と予測モデルの生成
fuyu_quant0
0
120
Celebrate UTIG: Staff and Student Awards 2024
utig
0
610
非同期コミュニケーションの構造 -チャットツールを用いた組織における情報の流れの設計について-
koisono
0
230
Tensor Representations in Signal Processing and Machine Learning (Tutorial at APSIPA-ASC 2020)
yokotatsuya
0
150
Planted Clique Conjectures are Equivalent
nobushimi
0
130
MoveItを使った産業用ロボット向け動作作成方法の紹介 / Introduction to creating motion for industrial robots using MoveIt
ry0_ka
0
350
Trend Classification of InSAR Displacement Time Series Using SAE–CNN
satai
3
190
Featured
See All Featured
Writing Fast Ruby
sferik
628
61k
Testing 201, or: Great Expectations
jmmastey
42
7.4k
Unsuck your backbone
ammeep
669
57k
BBQ
matthewcrist
88
9.5k
Reflections from 52 weeks, 52 projects
jeffersonlam
349
20k
For a Future-Friendly Web
brad_frost
176
9.6k
CSS Pre-Processors: Stylus, Less & Sass
bermonpainter
356
30k
How GitHub (no longer) Works
holman
314
140k
Practical Orchestrator
shlominoach
186
10k
Building Adaptive Systems
keathley
40
2.5k
CoffeeScript is Beautiful & I Never Want to Write Plain JavaScript Again
sstephenson
160
15k
Bash Introduction
62gerente
611
210k
Transcript
Scaling Science
[email protected]
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
[email protected]