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Structural biology in the clouds:
A success story of 10 years
Alexandre Bonvin
Utrecht University
The Netherlands
a.m.j.j.bonvin@uu.nl
Solution NMR: 950, 900-cryo, 750, 600-cryo, 600US, 2x500 MHz
Solid-state NMR: 800WB-DNP, 400WB-DNP, 700US, 500WB MHz
e-infrastructure: >1900 CPU cores + EGI grid (>110’000 CPU cores)
2019?: 1.2 GHz
National and European infrastructure
The molecular machines of life
NMR and structural biology
A	SpronkNMR production
The protein-protein interaction Cosmos
Understanding molecular recognition at atomic details
Solving molecular puzzles
by computational docking
haddock.science.uu.nl
>10000 users worldwide
Used by major pharma companies
Haddock
web portal
• > 10500 registered users
• > 188000 served runs
since June 2008
• > 35% on the GRID
Visit bonvinlab.org/software
De Vries et al. Nature Prot. 2010
Van Zundert et al. J.Mol.Biol. 2016
HADDOCK’s user base
10 years of serving the research
community
Made possible via HTC resources
provided by H2020 e-Infrastructure
projects over the years
European Open Science Cloud
CC
Under EGI-Engage
The eInfrastructure landscape over the years
European Open Science Cloud
CC
Under EGI-Engage
The eInfrastructure landscape over the years
Virtual Research Community
# Number of dimensions 2
# INAME 1 1H
# INAME 2 1H
12 2.137 2.387 1 T 0.000e+00 0.00e+00 - 0 2756 2760 0
14 2.387 4.140 1 T 0.000e+00 0.00e+00 - 0 2760 2752 0
32 1.849 4.432 1 T 0.000e+00 0.00e+00 - 0 2259 2257 0
36 1.849 3.143 1 T 0.000e+00 0.00e+00 - 0 2259 2587 0
39 1.760 4.432 1 T 0.000e+00 0.00e+00 - 0 2260 2257 0
40 1.760 1.849 1 T 0.000e+00 0.00e+00 - 0 2260 2259 0
43 1.760 3.143 1 T 0.000e+00 0.00e+00 - 0 2260 2587 0
46 1.649 4.432 1 T 1.035e+05 0.00e+00 r 0 2583 2257 0
47 1.649 1.849 1 T 0.000e+00 0.00e+00 - 0 2583 2259 0
assign ( resid 501 and name OO )
( resid 501 and name Z )
( resid 501 and name X )
( resid 501 and name Y )
( resid 2 and name CA ) -0.1400 0.15000
assign ( resid 501 and name OO )
( resid 501 and name Z )
( resid 501 and name X )
( resid 501 and name Y )
( resid 3 and name CA ) -0.0100 0.15000
Data
interpretation
Structure, dynamics & interactions
è impact on research and health:
- origin of disease
- design of new experiments
- drug design…
Exploiting GRID resources in structural biology…
Computations
NMR data collection and processing SAXS data analysis
eScience hub for NMR and structural biology
Infrastructure
Science
Com
m
unity
Knowledge
The WeNMR VRC
www.wenmr.eu
WeNMR VRC (February 2018)
• enmr.eu: One of the largest (#users) VO in life sciences
• >830 users have registered so far(36% outside EU)
• Support from >40 sites for >200’000 CPU cores via EGI infrastructure
• User-friendly access to Grid via web portals
• Supported by an SLA (2016, updated in 2017) with EGI and NGIs
www.wenmr.eu
NMR
SAXSA worldwide
e-Infrastructure for NMR and
structural biology
Sustained growth of the WeNMR VRC
0
250
500
750
1000
1250
1500
1750
2000
2250
2500
2750
3000
3250
3500
2011-01
2011-04
2011-07
2011-10
2012-01
2012-04
2012-07
2012-10
2013-01
2013-04
2013-07
2013-10
2014-01
2014-04
2014-07
2014-10
2015-01
2015-04
2015-07
2015-10
2016-01
2016-04
2016-07
2016-10
2017-01
2017-04
2017-07
2017-10
2018-01
0
50000
100000
150000
200000
250000
300000
350000
400000
450000
500000
2009/1
2009/4
2009/7
2009/10
2010/1
2010/4
2010/7
2010/10
2011/1
2011/4
2011/7
2011/10
2012/1
2012/4
2012/7
2012/10
2013/1
2013/4
2013/7
2013/10
2014/1
2014/4
2014/7
2014/10
2015/1
2015/4
2015/7
2015/10
2016/1
2016/4
2016/7
2016/10
2017/1
2017/4
2017/7
2017/10
enmr.eu VO grid jobs
Operational since 10 years
End of
WeNMR
funding
Start of EGI-Engage
Start of West-Life
End of
eNMR
~2400 normalized CPU years over 2017
Challenges & e-Solutions
§ Attract users!
§ Offer them top of the line eScience solutions for
their research ... which means top of the line
software
The WeNMR VRC
Knowledge
Help Center
Tutorials, Wiki
Consultancy
Services
Portals
VRC
Third-party aggregation
Grid
Exposure
Marketplace
Blogs, news,
events..
User
SSO
Facebook
• 39 web portals (31 NMR, 7 SAXS)
• of which 29 by partners
• Uniform access through the new
Single Sign On functionality
• RPC access available for some
portals
The WeNMR services portfolio
Challenges & e-Solutions
§ Attract users!
§ Offer them top of the line eScience solutions for
their research ... which means top of the line
software
§ Provide them training, tutorials and support
The WeNMR VRC
Knowledge
Help Center
Tutorials, Wiki
Consultancy
Services
Portals
VRC
Third-party aggregation
Grid
Exposure
Marketplace
Blogs, news,
events..
User
SSO
Facebook
• Help center
• Consultancy remote or on
location
• Tutorials, wiki documents,
movies
• YouTube channel
• Many workshops …
Challenges & e-Solutions
§ Attract users!
§ Offer them top of the line eScience solutions for
their research ... which means top of the line
softwares)
§ Provide them training, tutorials and support
§ Make their life easier
The WeNMR VRC
Knowledge
Help Center
Tutorials, Wiki
Consultancy
Services
Portals
VRC
Third-party aggregation
Grid
Exposure
Marketplace
Blogs, news,
events..
User
SSO
Facebook
Challenges & e-Solutions
§ Attract users!
§ Access to e-infrastructure
Distribution of resources
World-wide: Dec 2015 ~ 140’000 CPU cores from 42 sites
(EGI & OSG)
• Support for
WeNMR/MoBrain/West-Life
activities
• 75M CPU hours, 50 TB storage from
7 sites
• Renewed in 2017
– CESNET-MetaCloud (Czech Republic)
– INFN-PADOVA (Italy)
– IFCA-LCG2 (Spain)
– NCG-INGRID-PT (Portugal)
– NIKHEF (The Netherlands)
– RAL-LCG2 (UK)
– SURFsara (The Netherlands)
– TW-NCHC (Taiwan)
SLA agreement
Challenges & e-Solutions
§ Attract users!
§ Access to e-infrastructure
§ Job management / submission
Job management
§ Need to handle millions of job submission
§ Initially based on gLite
§ Mostly migrated to DIRAC4EGI
§ From a user perspective DIRAC is in principle
grid/cloud agnostic:
§ Can automatically launch VMs
§ Software distributed via CVMFS
But where is the CLOUD?
European Open Science Cloud
CC
Under EGI-Engage
The eInfrastructure landscape over the years
§ With activities toward:
§ Integrating the communities
§ Making best use of cloud resources
§ Bringing data to the cloud (cryo-EM)
§ Exploiting GPGPU resources
§ While maintaining the quality of our
current services!
The MoBrain CC under EGI Engage
The West-Life VRE
west-life.eu
The West-Life-VRE portal
• Virtual machines
Already used for several workshops
(INSTRUCT Utrecht Apr. 2016; NeCEN, Leiden, ISCG2017, Taipei)
SCIPION cloud server now in production
Cryo-EM in the clouds
Virtualising portals using EC3
EGI Fedcloud usage
0
20
40
60
80
100
120
140
160
2016/12016/22016/32016/42016/52016/62016/72016/82016/92016/102016/112016/122017/12017/22017/32017/42017/52017/62017/72017/82017/92017/102017/112017/12
enmr.eu VO number of VMs
Average of 21 VMs full time per month
Harvesting GPGPU resources
Exploring GPGPU resources: PowerFit
• Python package to
automatically fit high-
resolution biomolecular
structures into cryo-EM
densities
• Simple command-line
program, able to run using
single/multiple CPUs or GPU
van Zundert and Bonvin. AIMS Biophysics 2, 73-87 (2015)
www.github.com/haddocking/powerfit
Exploring GPGPU resources: DisVis
• Python package to Python
package to visualize and
quantify the accessible
interaction space of distance
restrained binary biomolecular
complexes.
• Simple command-line
program, able to run using
single/multiple CPUs or GPU
van Zundert and Bonvin. Bioinformatics. 31, 3222-3224 (2015)
www.github.com/haddocking/disvis
Accessible interaction
space as a function of
the number of
distance restraints
DISVIS example
Baremetal vs grid vs cloud
ID Type GPU #Cores CPU type Mem (GB)
B-K20 Baremetal Tesla K20 24 HT (12 real) Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz 32
B-K40 Baremetal Tesla K40 48 HT (24 real) Intel(R) Xeon(R) CPU E5-2680 v3 @ 2.50GHz 512
D-K20 Docker on K20 Tesla K20 24 Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz 32
K-K40 KVM on K40 Tesla K40 24 Intel(R) Xeon(R) CPU E5-2680 v3 @ 2.50GHz 32
Case Machine
TimeGPU
(sec)
TimeCPU 1
core CPU1/GPU
B-K40 Baremetal 674 7928 11.8
K-K40 KVM 671 7996 11.9
B-K20 Baremetal 830 11839 14.3
D- K20 Docker 837 11926 14.3
No loss of performance
CourtesyofMarioDavid
INDIGO
<= Grid
<= Cloud
GPGPU, GRID-enabled web portals
http://milou.science.uu.nl/enmr/services/DISVIS/ http://milou.science.uu.nl/enmr/services/POWERFIT/
Pre-processing
+
Input files
packaging
Architecture behind the portals
User DB
User not found
Input error
WEB CLIENT WEB SERVER MASTER NODE WORKING NODE
GPU-
calculation
Validation
Submission
to local
nodes
Submission
to grid
node
CPU-
calculation
Chimera
image
generation
Post-processing
+
Results formatting
Output files
packaging
+
submission of
image generation
OR
Software Provisioning
indigodatacloudapps/disvis
indigodatacloudapps/powerfit
Because of complex
software dependencies
we use docker containers
• Python2.7
• NumPy 1.8+
• SciPy
• FFTW3
• pyFFTW 0.10+
• OpenCL1.1+
• pyopencl
• clFFT
• gpyfft
And to avoid security
issues on the grid side,
udocker from INDIGO
Some usage statsOperational since Aug. 2016
Published Dec. 2016 Top pulls in INDIGO applications docker hub
https://hub.docker.com/r/indigodatacloudapps/
Where are we going?
Which solution?
A bit of everything…
Thematic services under EOSC-Hub
https://www.egi.eu/use-cases/scientific-applications-tools/
Thematic services under EOSC-Hub
§ Harvest both
§ DIRAC4EGI can handle both without the
additional burden of managing the cloud
VMs
§ We still have much more grid than cloud
resources
§ HADDOCK portal as use case in Helix Nebula
Science Cloud
The exascale challenge
Ø ~20’000 human proteins
Ø Hundreds of thousands of interactions
Ø Billions CPU hours and exabytes of data
Ø Need to make our software ready for it!
bioexcel.eu
Acknowledgments:
the CSB group@UU
VICI
TOP-PUNT
WeNMR
West-Life
EGI-Engage
INDIGO-
Datacloud
BioExcel CoE
EOSC-Hub
SURFSara
€€
Thank you for your attention!
http://bonvinlab.org

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Structural biology in the clouds: A success story of 10 years

  • 1. Structural biology in the clouds: A success story of 10 years Alexandre Bonvin Utrecht University The Netherlands a.m.j.j.bonvin@uu.nl
  • 2. Solution NMR: 950, 900-cryo, 750, 600-cryo, 600US, 2x500 MHz Solid-state NMR: 800WB-DNP, 400WB-DNP, 700US, 500WB MHz e-infrastructure: >1900 CPU cores + EGI grid (>110’000 CPU cores) 2019?: 1.2 GHz National and European infrastructure
  • 4. NMR and structural biology A SpronkNMR production
  • 7. Solving molecular puzzles by computational docking haddock.science.uu.nl >10000 users worldwide Used by major pharma companies
  • 8. Haddock web portal • > 10500 registered users • > 188000 served runs since June 2008 • > 35% on the GRID Visit bonvinlab.org/software De Vries et al. Nature Prot. 2010 Van Zundert et al. J.Mol.Biol. 2016
  • 10.
  • 11.
  • 12. 10 years of serving the research community Made possible via HTC resources provided by H2020 e-Infrastructure projects over the years
  • 13. European Open Science Cloud CC Under EGI-Engage The eInfrastructure landscape over the years
  • 14. European Open Science Cloud CC Under EGI-Engage The eInfrastructure landscape over the years
  • 16. # Number of dimensions 2 # INAME 1 1H # INAME 2 1H 12 2.137 2.387 1 T 0.000e+00 0.00e+00 - 0 2756 2760 0 14 2.387 4.140 1 T 0.000e+00 0.00e+00 - 0 2760 2752 0 32 1.849 4.432 1 T 0.000e+00 0.00e+00 - 0 2259 2257 0 36 1.849 3.143 1 T 0.000e+00 0.00e+00 - 0 2259 2587 0 39 1.760 4.432 1 T 0.000e+00 0.00e+00 - 0 2260 2257 0 40 1.760 1.849 1 T 0.000e+00 0.00e+00 - 0 2260 2259 0 43 1.760 3.143 1 T 0.000e+00 0.00e+00 - 0 2260 2587 0 46 1.649 4.432 1 T 1.035e+05 0.00e+00 r 0 2583 2257 0 47 1.649 1.849 1 T 0.000e+00 0.00e+00 - 0 2583 2259 0 assign ( resid 501 and name OO ) ( resid 501 and name Z ) ( resid 501 and name X ) ( resid 501 and name Y ) ( resid 2 and name CA ) -0.1400 0.15000 assign ( resid 501 and name OO ) ( resid 501 and name Z ) ( resid 501 and name X ) ( resid 501 and name Y ) ( resid 3 and name CA ) -0.0100 0.15000 Data interpretation Structure, dynamics & interactions è impact on research and health: - origin of disease - design of new experiments - drug design… Exploiting GRID resources in structural biology… Computations NMR data collection and processing SAXS data analysis
  • 17. eScience hub for NMR and structural biology Infrastructure Science Com m unity Knowledge The WeNMR VRC
  • 19. WeNMR VRC (February 2018) • enmr.eu: One of the largest (#users) VO in life sciences • >830 users have registered so far(36% outside EU) • Support from >40 sites for >200’000 CPU cores via EGI infrastructure • User-friendly access to Grid via web portals • Supported by an SLA (2016, updated in 2017) with EGI and NGIs www.wenmr.eu NMR SAXSA worldwide e-Infrastructure for NMR and structural biology
  • 20. Sustained growth of the WeNMR VRC 0 250 500 750 1000 1250 1500 1750 2000 2250 2500 2750 3000 3250 3500 2011-01 2011-04 2011-07 2011-10 2012-01 2012-04 2012-07 2012-10 2013-01 2013-04 2013-07 2013-10 2014-01 2014-04 2014-07 2014-10 2015-01 2015-04 2015-07 2015-10 2016-01 2016-04 2016-07 2016-10 2017-01 2017-04 2017-07 2017-10 2018-01
  • 22. Challenges & e-Solutions § Attract users! § Offer them top of the line eScience solutions for their research ... which means top of the line software
  • 23. The WeNMR VRC Knowledge Help Center Tutorials, Wiki Consultancy Services Portals VRC Third-party aggregation Grid Exposure Marketplace Blogs, news, events.. User SSO Facebook • 39 web portals (31 NMR, 7 SAXS) • of which 29 by partners • Uniform access through the new Single Sign On functionality • RPC access available for some portals
  • 24. The WeNMR services portfolio
  • 25. Challenges & e-Solutions § Attract users! § Offer them top of the line eScience solutions for their research ... which means top of the line software § Provide them training, tutorials and support
  • 26. The WeNMR VRC Knowledge Help Center Tutorials, Wiki Consultancy Services Portals VRC Third-party aggregation Grid Exposure Marketplace Blogs, news, events.. User SSO Facebook • Help center • Consultancy remote or on location • Tutorials, wiki documents, movies • YouTube channel • Many workshops …
  • 27. Challenges & e-Solutions § Attract users! § Offer them top of the line eScience solutions for their research ... which means top of the line softwares) § Provide them training, tutorials and support § Make their life easier
  • 28. The WeNMR VRC Knowledge Help Center Tutorials, Wiki Consultancy Services Portals VRC Third-party aggregation Grid Exposure Marketplace Blogs, news, events.. User SSO Facebook
  • 29. Challenges & e-Solutions § Attract users! § Access to e-infrastructure
  • 30. Distribution of resources World-wide: Dec 2015 ~ 140’000 CPU cores from 42 sites (EGI & OSG)
  • 31. • Support for WeNMR/MoBrain/West-Life activities • 75M CPU hours, 50 TB storage from 7 sites • Renewed in 2017 – CESNET-MetaCloud (Czech Republic) – INFN-PADOVA (Italy) – IFCA-LCG2 (Spain) – NCG-INGRID-PT (Portugal) – NIKHEF (The Netherlands) – RAL-LCG2 (UK) – SURFsara (The Netherlands) – TW-NCHC (Taiwan) SLA agreement
  • 32. Challenges & e-Solutions § Attract users! § Access to e-infrastructure § Job management / submission
  • 33. Job management § Need to handle millions of job submission § Initially based on gLite § Mostly migrated to DIRAC4EGI § From a user perspective DIRAC is in principle grid/cloud agnostic: § Can automatically launch VMs § Software distributed via CVMFS
  • 34. But where is the CLOUD?
  • 35. European Open Science Cloud CC Under EGI-Engage The eInfrastructure landscape over the years
  • 36. § With activities toward: § Integrating the communities § Making best use of cloud resources § Bringing data to the cloud (cryo-EM) § Exploiting GPGPU resources § While maintaining the quality of our current services! The MoBrain CC under EGI Engage
  • 38. The West-Life-VRE portal • Virtual machines Already used for several workshops (INSTRUCT Utrecht Apr. 2016; NeCEN, Leiden, ISCG2017, Taipei)
  • 39. SCIPION cloud server now in production Cryo-EM in the clouds
  • 43. Exploring GPGPU resources: PowerFit • Python package to automatically fit high- resolution biomolecular structures into cryo-EM densities • Simple command-line program, able to run using single/multiple CPUs or GPU van Zundert and Bonvin. AIMS Biophysics 2, 73-87 (2015) www.github.com/haddocking/powerfit
  • 44. Exploring GPGPU resources: DisVis • Python package to Python package to visualize and quantify the accessible interaction space of distance restrained binary biomolecular complexes. • Simple command-line program, able to run using single/multiple CPUs or GPU van Zundert and Bonvin. Bioinformatics. 31, 3222-3224 (2015) www.github.com/haddocking/disvis
  • 45. Accessible interaction space as a function of the number of distance restraints DISVIS example
  • 46. Baremetal vs grid vs cloud ID Type GPU #Cores CPU type Mem (GB) B-K20 Baremetal Tesla K20 24 HT (12 real) Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz 32 B-K40 Baremetal Tesla K40 48 HT (24 real) Intel(R) Xeon(R) CPU E5-2680 v3 @ 2.50GHz 512 D-K20 Docker on K20 Tesla K20 24 Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz 32 K-K40 KVM on K40 Tesla K40 24 Intel(R) Xeon(R) CPU E5-2680 v3 @ 2.50GHz 32 Case Machine TimeGPU (sec) TimeCPU 1 core CPU1/GPU B-K40 Baremetal 674 7928 11.8 K-K40 KVM 671 7996 11.9 B-K20 Baremetal 830 11839 14.3 D- K20 Docker 837 11926 14.3 No loss of performance CourtesyofMarioDavid INDIGO <= Grid <= Cloud
  • 47. GPGPU, GRID-enabled web portals http://milou.science.uu.nl/enmr/services/DISVIS/ http://milou.science.uu.nl/enmr/services/POWERFIT/
  • 48. Pre-processing + Input files packaging Architecture behind the portals User DB User not found Input error WEB CLIENT WEB SERVER MASTER NODE WORKING NODE GPU- calculation Validation Submission to local nodes Submission to grid node CPU- calculation Chimera image generation Post-processing + Results formatting Output files packaging + submission of image generation OR
  • 49. Software Provisioning indigodatacloudapps/disvis indigodatacloudapps/powerfit Because of complex software dependencies we use docker containers • Python2.7 • NumPy 1.8+ • SciPy • FFTW3 • pyFFTW 0.10+ • OpenCL1.1+ • pyopencl • clFFT • gpyfft And to avoid security issues on the grid side, udocker from INDIGO
  • 50. Some usage statsOperational since Aug. 2016 Published Dec. 2016 Top pulls in INDIGO applications docker hub https://hub.docker.com/r/indigodatacloudapps/
  • 51. Where are we going?
  • 53. A bit of everything…
  • 54. Thematic services under EOSC-Hub https://www.egi.eu/use-cases/scientific-applications-tools/
  • 55. Thematic services under EOSC-Hub § Harvest both § DIRAC4EGI can handle both without the additional burden of managing the cloud VMs § We still have much more grid than cloud resources § HADDOCK portal as use case in Helix Nebula Science Cloud
  • 56. The exascale challenge Ø ~20’000 human proteins Ø Hundreds of thousands of interactions Ø Billions CPU hours and exabytes of data Ø Need to make our software ready for it! bioexcel.eu
  • 58. Thank you for your attention! http://bonvinlab.org