Scaling Science

Introducing five principles for reproducibility.

Transcript

1. Scaling Science matthew@amazon.com Dr. Matt Wood
2. Hello
3. Science
4. Beautiful, unique.
5. Impossible to re-create
6. Snowflake Science
7. Reproducibility
8. Reproducibility scales science
9. Reproduce. Reuse. Remix.
10. Value++
12. How do we get from here to there? 5PRINCIPLES REPRODUCIBILITY OF
13. 1. Data has Gravity 5 PRINCIPLES REPRODUCIBILITY OF
14. Increasingly large data collections
15. 1000 Genomes Project: 200Tb
16. Challenging to obtain and manage
17. Expensive to experiment
18. Large barrier to reproducibility
19. Data size will increase
20. Data integration will increase
21. Data dependencies will increase
22. Move data to the users
23. Move data to the users X
24. Move tools to the data
25. Place data where it can consumed by tools
26. Place tools where they can access data
30. Canonical source
32. More data, more users, more uses, more locations
33. Cost
34. Force multiplier
35. Cost
36. Complexity
37. Cost and complexity kill reproducibility
38. Utility computing
39. Availability
40. Pay-as-you-go
41. Flexibility
42. Performance
43. CPU + IO
44. Intel Xeon E5 NVIDIA Tesla GPUs
45. 240 TFLOPS
46. 90 - 120k IOPS on SSDs
47. Performance through productivity
48. Cost
49. On-demand access
50. Reserved capacity
51. 100% Reserved capacity
52. 100% Reserved capacity On-demand
53. 100% Reserved capacity On-demand
54. Spot instances
55. Utility computing enhanced reproducibility
57. 2. Ease of use is a pre-requisite 5 PRINCIPLES REPRODUCIBILITY OF
58. http://headrush.typepad.com/creating_passionate_users/2005/10/getting_users_p.html
59. Help overcome the suck threshold
60. Easy to embrace and extend
61. Choose the right abstraction for the user
62. $ ec2-run-instances
63. $ starcluster start
65. Package and automate
66. Package and automate Amazon machine images, VM import
67. Package and automate Amazon machine images, VM import Deployment scripts, CloudFormation, Chef, Puppet
68. Expert-as-a-service
71. 1000 Genomes Cloud BioLinux
73. Your HiSeq data Illumina BaseSpace
74. Architectural freedom
75. Freedom of abstraction
76. 3. Reuse is as important as reproduction 5 PRINCIPLES REPRODUCIBILITY OF
77. Seven Deadly sins of Bioinformatics: http://www.slideshare.net/dullhunk/the-seven-deadly-sins-of-bioinformatics
78. Seven Deadly sins of Bioinformatics: http://www.slideshare.net/dullhunk/the-seven-deadly-sins-of-bioinformatics
79. Infonauts are hackers
80. They have their own way of working
81. The ‘Big Red Button’
82. Fire and forget reproduction is a good first step, but limits longer term value.
83. Monolithic, one-stop-shop
84. Work well for intended purpose
85. Challenging to install, dependency heavy
86. Di cult to grok
87. Inflexible
88. Infonauts are hackers: embrace it.
89. Small things. Loosely coupled.
90. Easier to grok
91. Easier to reuse
92. Easier to integrate
93. Lower barrier to entry
94. Scale out
95. Build for reuse. Be remix friendly. Maximize value.
96. 4. Build for collaboration 5 PRINCIPLES REPRODUCIBILITY OF
97. Workflows are memes
98. Reproduction is just the first step
99. Bill of materials: code, data, configuration, infrastructure
100. Full definition for reproduction
101. Utility computing provides a playground for bioinformatics
102. Code + AMI + custom datasets + public datasets + databases + compute + result data
103. Code + AMI + custom datasets + public datasets + databases + compute + result data
104. Code + AMI + custom datasets + public datasets + databases + compute + result data
105. Code + AMI + custom datasets + public datasets + databases + compute + result data
106. Package, automate, contribute.
107. Utility platform provides scale for production runs
108. Drug discovery on 50k cores: Less than $1000
109. 5. Provenance is a first class object 5 PRINCIPLES REPRODUCIBILITY OF
110. Versioning becomes really important
111. Especially in an active community
112. Doubly so with loosely coupled tools
113. Provenance metadata is a first class entity
114. Distributed provenance
115. 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
117. Thank you aws.amazon.com @mza matthew@amazon.com