1. e-Science and Grid The VL-e approach L.O. (Bob) Hertzberger Computer Architecture and Parallel Systems Group Department of Computer Science Universiteit van Amsterdam bob@science.uva.nl

2. Content Developments in Grid Developments in e-Science Objectives Virtual Lab for e-Science Research philosophy Conclusions

3. Background ICTpush developments Processing power doubles every 18 month Memory size doubles every 12 month Network speed doubles every 9 month Something has to be done to harness this development Virtualization of ICT resources  Internet  Web  Grid

4. Internal versus external bandwidth Mbit/s Computer busses networks

5. Web& Grid & Web/Grid Services Web services is a paradigm/way of using/accessing information Web resources are mostly human-centered (understood by humans, computers can read but can’t understand) Grid is about accessing & sharing computing resources by virtualization Data & Information repositories Experimental facilities OGSA: Service oriented Grid standard based on Web services

6. Web & Grid & Semantic Web/Grid Semantic Web resources should also be understandable by computers This way, many complex tasks formulated and assigned by humans can be automated and executed by agents working on the Semantic Web But, both Grid and Web Services only focus on single services. Semantic Web/Grid should be able to describe a single service as well as the relationship between services e.g. aggregate service using a number of services so making knowledge explicit

7. Levels of Grid abstraction Computational Grid Data Grid Information Web/Grid Knowledge Web/Grid

8. Background information experimental sciences There is a tendency to look ever deeper in: Matter e.g. Physics Universe e.g. Astronomy Life e.g. Life sciences Therefore experiments become increasingly more complex Instrumental consequences are increase in detector: Resolution & sensitivity Automation & robotization Results for instance in life science in:  So called high throughput methods  Omics experimentation  genome ===> genomics

9. New technologies in Life Sciences research University of Amsterdam cell GenomicsTranscriptomicsProteomicsMetabolomics RNA protein metabolites DNA Methodology/ Technology

10. Paradigm shift in Life science Past experiments where hypothesis driven Evaluate hypothesis Complement existing knowledge Present experiments are data driven Discover knowledge from large amounts of data  Apply statistical techniques

11. Background information experimental sciences Experiments become increasingly more complex Driven by detector developments  Resolution increases  Automation & robotization increases Results in an increase in amount and complexity of data

12. The Application data crisis Scientific experiments start to generate lots of data medical imaging (fMRI): ~ 1 GByte per measurement (day) Bio-informatics queries:500 GByte per database Satellite world imagery: ~ 5 TByte/year Current particle physics: 1 PByte per year LHC physics (2007): 10-30 PByte per year Data is often very distributed

13. Background information experimental sciences Experiments become increasingly more complex Driven by detector developments  Resolution increases  Automation & robotization increases Results in an increase in amount and complexity of data Something has to be done to harness this development Virtualization of experimental resources: e-Science

14. The what of e-Science e-Science is the application domain “Science” of Grid & Web More than only coping with data explosion A multi-disciplinary activity combining human expertise & knowledge between:  A particular domain scientist  ICT scientist e-Science demands a different approach to experimentation because computer is integrated part of experiment  Consequence is a radical change in design for experimentation e-Science should apply and integrate Web/Grid methods where and whenever possible

15. Grid and Web Services Convergence Grid Definition of Web Service Resource Framework(WSRF) makes explicit distinction between “service” and stateful entities acting upon service i.e. the resources Means that Grid and Web communities can move forward on a common base!!! WSRF Started far apart in apps & tech OGSI GT2 GT1 HTTP WSDL, WS-* WSDL 2, WSDM Have been converging Ref: Foster Web

16. e-Science Objectives It should enhance the scientific process by: Stimulating collaboration by sharing data & information Result is re-use of data & information

17. The data sharing potential for Cognition Collaborative scientific research Information sharing Metadata modeling Allows for experiment validation Independent confirmation of results Statistical methodologies Access to large collections of data and metadata Training Train the next generation using peer reviewed publications and the associated data

18. Acquisition Alignment Reconstruction Segmentation Interpretation Electron tomography data pipeline

19. Cell Centred Database NCIMR (San Diego) Maryann Martone and Mark Ellisman

20. e-Science Objectives It should enhance the scientific process by: Stimulating collaboration by sharing data & information Improve re-use of data & information Combing data and information from different modalities Sensor data & information fusion

25. e-Science Objectives It should enhance the scientific process by: Stimulating collaboration by sharing data & information Improve re-use of data & information Combing data and information from different modalities  Sensor data & information fusion Realize the combination of real life & (model based) simulation experiments

26. An example of the combination of real life & (model based) simulation experiments patient specific vascular geometry (from CT) Segmentation blood flow simulation (Latice Bolzmann) Pre-operative planning (interaction) Suitable for parallelization through functional decomposition Simulated “Fem-Fem” bypass Patient’s vascular geometry (CTA) Grid resources Simulated Vascular Reconstruction in a Virtual Operating Theatre

27. e-Science Objectives It should enhance the scientific process by: Stimulating collaboration by sharing data & information Improve re-use of data & information Combing data and information from different modalities  Sensor data & information fusion Realize the combination of real life & (model based) simulation experiments Modeling of dynamic systems

28. Dynamic bird behaviour MODELS Bird distributions Ensembles Calibration and Data assimilation Predictions and on-line warnings RADAR Bird behaviour in relation to weather and landscape

29. e-Science Objectives It should result in : Computer aided support for rapid prototyping of ideas Stimulate the creativity process It should realize that by creating & applying: New ICT methodologies and a computing infrastructure stimulating this From this ICT point of view it should support the following application steps: Design Development & realization Execution Analysis & interpretation We try to realize e-Science and their applications via the Virtual Lab for e-Science (VL-e) project

30. Virtual Lab for e-Science research Philosophy Multidisciplinary research & the development of related ICT infrastructure Generic application support Application cases are drivers for computer & computational science and engineering research

31. Grid Services Harness multi-domain distributed resources Management of comm. & computing VL-e Application Oriented Services Food Informatics Dutch Telescience Medical Diagnosis & Imaging VL-e project Bio- Informatics Data Insive Science/ LOFAR Bio- Diversity Data intensive sciences

32. Two sides of Bioinformatics as an e-Science The scientific responsibility to develop the underlying computational concepts and models to convert complex biological data into useful biological and chemical knowledge Technological responsibility to manage and integrate huge amounts of heterogeneous data sources from high throughput experimentation

33. Role of bioinformatics cell Data generation/validation Data integration/fusion Data usage/user interfacing GenomicsTranscriptomicsProteomicsMetabolomics Integrative/System Biology RNA protein metabolites DNA methodology bioinformatics

34. Virtual Lab for e-Science research Philosophy Multidisciplinary research and development of related ICT infrastructure Generic application support Application cases are drivers for computer & computational science and engineering research Problem solving partly generic and partly specific Re-use of components via generic solutions whenever possible

35. Grid/ Web Services Harness multi-domain distributed resources Management of comm. & computing Management of comm. & computing Management of comm. & computing Potential Generic part Potential Generic part Potential Generic part Application Specific Part Application Specific Part Application Specific Part Virtual Laboratory Application Oriented Services Application pull

36. Generic e-Science aspects Virtual Reality Visualization & user interfaces Modeling & Simulation Interactive Problem Solving Data & information management Data modeling dynamic work flow management Content (knowledge) management Semantic aspects Meta data modeling  Ontologies Wrapper technology Design for Experimentation

37. Virtual Lab for e-Science research Philosophy Multidisciplinary research and development of related ICT infrastructure Generic application support Application cases are drivers for computer & computational science and engineering research Problem solving partly generic and partly specific Re-use of components via generic solutions whenever possible Rationalization of experimental process Reproducible & comparable

38. Issues for a reproducible scientific experiment interpretation Rationalization of the experiment and processes via protocols processing processed data conversion, filtering, analyses, simulation, … experiment parameters/settings, algorithms, intermediate results, … Parameter settings, Calibrations, Protocols … software packages, algorithms … raw data acquisition sensors,amplifiers imaging devices,, … presentation visualization, animation interactive exploration, … Metadata Much of this is lost when an experiment is completed.

39. Scientific experiments & e-Science Step1: designing an experiment Step2: performing the experiment Step3: analyzing the experiment results success For complex experiments:  contain complex processes  require interdisciplinary expertise  need large scale resource Grid & high level support

40. Experiment Topology –Graphical representation of self-contained data processing modules attached to each other in a workflow Process-Flow Templates(PFT) – Derived from ontologies – Graphical representation of data elements and processing steps in an experimental procedure – Information to support context-sensitive assistance (semantics) Study – Descriptions of experimental steps represented as an instance of a PFT with references to experiment topologies Components in a VL-e experiment

41. Step1: designing an experiment Step2: performing the experiment Step3: analyzing the experiment results success Taverna, Kepler, and Triana: model processes for computing tasks. In VL-e: model both computing tasks and human activity based processes, and model them from the perspective of an entire lifecycle. It tries to support Collaboration in different stages Information sharing Reuse of experiment PFT in VLAMGPFT instances in VLAMG Process Flow Template e-Science environment Scientific Workflow Management Systems Process Flow Template

42. Definition of experiment protocols Workflow definitions Recreate complex experiments into process flows Workflow execution Maintain control over the experiment Data processing execution Topologies of data processing modules Interpretation Visualization of processed results to help intuition Ontology definitions can help in obtaining a well-structured definition of experiment, data and metadata.

43. Virtual Lab for e-Science research Philosophy Multidisciplinary research and development of related ICT infrastructure Generic application support Application cases are drivers for computer & computational science and engineering research Problem solving partly generic and partly specific Re-use of components via generic solutions whenever possible Rationalization of experimental process Reproducible & comparable Two research experimentation environments Proof of concept for application experimentation Rapid prototyping for computer & computational science experimentation

44. The VL-e infrastructure Grid Middleware Surfnet Application specific service Application Potential Generic service & Virtual Lab. services Grid & Network Services Virtual Laboratory VL-e Proof of Concept Environment Telescience Medical Application Bio Informatics Applications VL-e Experimental Environment Virtual Lab. rapid prototyping (interactive simulation) Additional Grid Services (OGSA services) Network Service (lambda networking) VL-e Certification Environment Test & Cert. Compatibility Test & Cert. Grid Middleware Test & Cert. VL-software

45. Infrastructure for Applications Applications are a driving force of the PoC Experience shows applications value stability Foster two-way interaction to make this happen

46. Taverna, Kepler, Triana and VLAM. Step1: designing an experiment Step2: performing the experiment Step3: analyzing the experiment results success PFT e-Science environment Scientific Workflow Management Systems SWMS Research activity to be developed in the Rapid Prototyping environment Stable developments to be used in VL-e in the Proof of Concept environment Research activity to be developed in the Rapid Prototyping environment PFT

47. VL-e PoC environment Latest certified stable software environment of core grid and VL-e services Core infrastructure built around clusters and storage at SARA and NIKHEF (‘production’ quality) Controlled extension to other platforms and distributions On the user end: install needed servers: user interface systems, storage elements for data disclosure, grid-secured DB access Focus on stability and scalability

48. Hosted services for VL-e Key services and resources are offered centrally for all applications in VL-e Mass data and number crunching on the large resources at SARA Storage for data replication & distribution Persistent ‘strategic’ storage on tape Resource brokers, resource discovery, user group management

49. Why such a complex scheme? “software is part of the infrastructure” stability of core software needed to develop the new scientific applications enable distributed systems management (who runs what version when?) “the grid is one big error amplifier” “computers make mistakes like humans, only much, much faster”

50. What did we learn It is not enough to just transport current applications to Grid or e-Science infrastructures To fully exploit the potential of e-Science infrastructures one has to learn what is possible Therefore the full lifecycle of an experiment has to be taken into account Workflow management is a first step We add semantic information via Process Flow Templates Application innovation such as for instance bio- banking should be the aim Grids should be transparent for the end-user

51. Conclusions e-Science is a lot more more than trying to cope with data explosion alone Implementation of e-Science systems requires further rationalization and standardization of experimentation process e-Science success demands the realization of an environment allowing application driven experimentation & rapid dissemination of feed back of these new methods We try to do that via development of Proof of Concept based on Grid

52. Electron tomography data pipeline development TOM (Matlab) acquisition and data storage with the SRB SRB Storage With the CCDB database for retrieval of experimental data sets Grid-computing 3D reconstruction refinements Currently for EMAN and later also for TOM (Matlab) SARA - Amsterdam 1 Gbit/s