At this section a list of publications at proceedings of international conferences, journals and other events is presented. Part of  the research presented at the following publications has done, by exploiting the  HellasGrid/EGI grid infrastructure.

Journals

1. I. Kanaris, V. Mylonakis, A. Chatziioannou, I. Maglogiannis, and J.Soldatos, “HECTOR: Enabling Microarray Experiments over the Hellenic Grid Infrastructure”, J. Grid Comput., vol. 7, no. 3, pp. 1–22, Aug. 20
2. A. Chatziioannou, I. Kanaris, C. Doukas, P. Moulos, F.N. Kolisis and I. Maglogiannis, GRISSOM Platform: Enabling distributed Processing and Management of Biological Data through fusion of Grid and Web Technologies, IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, 2011 15 (1), art. no. 5638146, pp. 83-92.
3. K.Moutselos, I.Kanaris, A.Chatziioannou, I. Maglogiannis, F.N. Kolisis, KEGGconverter: a tool for the in-silico modelling of metabolic networks of the KEGG Pathways database (BMC Bioinformatics 10:324), 2009. (featured article of the volume, characterized as Highly Accessed).
4. V.C. Papadimitriou, Y.G. Lazarou, R.K. Talukdar, J.B. Burkholder, J. “Atmospheric Chemistry of CF3CF=CH2 and (Z)-CF3CF=CHF: Cl and NO3 Rate Coefficients, Cl Reaction Product Yields, and Thermochemical Calculations”  Phys. Chem A2011, 115, 167 – 181.
5. D.Achlioptas, R.M. D’Souza and J. Spencer, Explosive Percolation in Random Networks, Science 323,p. 1453 ,(2009)
6. R.A. da Costa, S.N.Dorogovtsev, A.V.Goltsev, and J.F.F.Mendes, Explosive Percolation” Transition is Actually Continuous, Physical Review Letters 105(25),255701,(2010)
7. P.Grassberger, C. Christensen, G. Bizhani, S-W Son, and M. Paczuski, Explosive Percolation is Continuous, but with Unusual Finite Size Behavior, Phys Rev Lett. 106(22), (2011)
8. O. Riordan and L. Warnke, Explosive percolation is continuous, Science 333 (2011)
9. R.M. Ziff, Explosive growth in biased dynamic percolation on two-dimensional regular lattice networks, Physical Review Letters 103(4),45701,(2009)
10. F. Radicchi and S. Fortunato, Explosive Percolation: A numerical analysis, Physical Review E 81(3),036110,(2010)
11. N.A.M. Araújo and H.J.Hermann, Explosive Percolation via Control of the Largest Cluster, Physical Review Letters 105(3),035701,(2010)
12. L. Tsetseris, “Stability and dynamics of carbon and nitrogen dopants in anatase TiO2”, Physical Review B 81, 165205 (2010).
13. L. Tsetseris, “Configurations, electronic properties, and diffusion of carbon and nitrogen dopants in rutile TiO2”,  Physical Review B 84, 165201 (2011).
14. L. Tsetseris, “Stability of Group-V Endohedral Fullerenes”, Journal of Physical Chemistry C 115, 3528 (2011).
15. G. Volonakis, L. Tsetseris, and S. Logothetidis, “Electronic and structural properties of TiB2: Bulk, surface, and nanoscale effects”, Materials Science and Engineering B 176, 484 (2011).
16. N. Kalfagiannis, G. Volonakis, L. Tsetseris, and S. Logothetidis, “Excess of boron in TiB2 superhard thin films: a combined experimental and ab initio study”, Journal of Physics D 44, 385402 (2011).
17. G. Volonakis, L. Tsetseris, and S. Logothetidis, “Impurity-related vibrational modes in a pentacene crystal”,  European Physical Journal: Applied Physics 55, 23903 (2011).
18. E. Golias, L. Tsetseris, A. Dimoulas, and S. T. Pantelides, “Ge volatilization products in high-k gate dielectrics”,  Microelectronic Engineering 88, 427 (2011).
19. E. Golias, L. Tsetseris, and A. Dimoulas, “Ge-related impurities in high-k oxides: Carrier traps and interaction with native defects”, Microelectronic Engineering 88, 1432 (2011).
20. Fotis E. Psomopoulos and Pericles A. Mitkas, “Bioinformatics Algorithm Development for Grid Environments”, Journal of Systems & Software, vol. 83, No 7. (2010), pp. 1249-1257.
21. Skarlatoudis A.A., C.B. Papazachos and N. Theodoulidis, (2011b). Site response study of the city of Thessaloniki (N. Greece), for the 04/07/1978 (M5.1) aftershock, using a 3D Finite-Difference wave propagation method, accepted for publication in Bull. Seism. Soc. Am.
22. V. Daskalakis, M. Giatromanolakis, M. Porrini, S. C. Farantos, and O. Gervasi, Computer Physics, Chapter: Grid computing multiple shooting algorithms for extended phase space sampling and long time propagation in Molecular Dynamics, Nova Science Publishing Co., 2011.
23. M. Porrini, V. Daskalakis, and S. C. Farantos, Thermodynamic Perturbation Calculations on Cytochrome c Oxidases interacting with small ligands, Phys. Chem. Chem. Phys., submitted, 2011.
24. Massimiliano Porrini, Vangelis Daskalakis, S. C. Farantos, and Constantinos Varotsis, Heme Cavity Dynamics of Photodissociated CO from ba3-Cytochrome c Oxidase: the Role of Ring-D Propionate, J. Phys. Chem. B, 113(35):12129-12135, 2009.
25. D. Kalles, and I. Fykouras. “Examples as Interaction: On Humans Teaching a Computer to Play a Game”, International Journal on Artificial Intelligence Tools, 2010.
26. Huszar P., K. Juda-Rezler, T. Halenka, H. Chervenkov, D. Syrakov, B. C. Krueger, P. Zanis, D. Melas, E. Katragkou, M. Reizer, W. Trapp, M. Belda, Potential climate change impacts on ozone and PM levels over Central and Eastern Europe from high resolution simulations, Climate Research (in press), 2011
27. Κatragkou E., P. Zanis, I. Kioutsioukis, I. Tegoulias, D. Melas, B.C. Krüger, E. Coppola, Future climate change impacts on summer surface ozone from regional climate-air quality simulations over Europe, J Geophys Res (in press), 2011
28. P. K. Gkonis, G. V. Tsoulos and D. I. Kaklamani, “An adaptive beam-shaping strategy for WCDMA multicellular networks with non-uniform traffic requirements”, Journal of Communications (JCM), vol. 3, issue 4, pp. 16-25, September 2008.
29. P. K. Gkonis, T. E. Athanaileas, G. V. Tsoulos, G. E. Athanasiadou, and D. I. Kaklamani, “Adaptive beam-centric admission control for WCDMA multicell/multiservice scenarios with non-uniform traffic”, Journal of Wireless Personal Communications (Kluwer), December 2009.
30. P. K. Gkonis, G. V. Tsoulos and D. I. Kaklamani, “Performance evaluation of MIMO-WCDMA cellular networks in multiuser frequency selective fading environments”, Wireless Communications and Mobile Computing (Wiley), article first published online: 18/01/2011, DOI:10.1002/wcm.1096.
31. T. E. Athanaileas, P. K. Gkonis, G. E. Athanasiadou, G. V. Tsoulos and D. I. Kaklamani, “Implementation and evaluation of a web-based grid-enabled environment for WCDMA multibeam system simulations”, IEEE Antennas and Propagation Magazine, vol. 50, issue 3, pp. 195-204, June 2008.
32. C. Simserides, A. Lipinska, K.N. Trohidou, T. Dietl, Reducing influence of antiferromagnetic interactions on ferromagnetic properties of p-(Cd,Mn)Te quantum wells, Physica E: Low-dimensional Systems and Nanostructures 42 (2010) 2694-2697
33. A. Lipinska, C. Simserides, K. N. Trohidou, M. Goryca, P. Kossacki, A. Majhofer, and T. Dietl, Ferromagnetic properties of p-(Cd,Mn)Te quantum wells: Interpretation of magneto-optical measurements by Monte Carlo simulations, Phys. Rev. B 79 (2009) 235322
34. K. Christodoulopoulos, V. Gkamas, E. Varvarigos, “Statistical Analysis and Modeling of jobs in a Grid Environment”, Springer Journal of Grid Computing 6 (1), pp. 77-101.
35. F. Georgatos , V. Gkamas , A. Ilias , G. Kouretis , E. Varvarigos , “A Grid-enabled CPU scavenging architecture and a case study of its use in the Greek School Network”, Springer Journal of Grid Computing, 8 (1), pp 61-75.
36. A.A. Skarlatoudis, C.B. Papazachos and N. Theodoulidis, Spatial distribution of site-effects and wave propagation properties in Thessaloniki (N. Greece) using a 3D Finite Difference method, Geoph. J. Int., 185, 485-513, (2011).
37.  A.A. Skarlatoudis, C.B. Papazachos, N. Theodoulidis, J. Kristek and P. Moczo, Local site-effects for the city of Thessaloniki (N. Greece) using a 3D Finite-Difference method:  A case of complex dependence on source and model parameters, Geoph. J. Int., 182,  279-298, (2010).
38. Tzanis, G., Kavakiotis, I., Vlahavas, I. (2011). PolyA-iEP: A Data Mining Method for the Effective Prediction of Polyadenylation Sites, Expert Systems with Applications, Elsevier, 38(10): 12398-12408.
39. Tzanis, G. Berberidis, C., and Vlahavas, I. (2012). StackTIS: A Stacked Generalization Approach for Effective Prediction of Translation Initiation Sites, Computers in Biology and Medicine, Elsevier, 42 (2012) 61–69.
40. Lagouvardos K., E. Floros and V. Kotroni, 2010: A Grid-enabled Regional-scale Ensemble Forecasting System in the Mediterranean. Journal of Grid Computing, 8:181–197.
41. Kotroni V., E.Floros, K. Lagouvardos, G. Pejanovic, L. Ilic, and M. Zivkovic, 2010: Multi-model multi-analysis ensemble weather forecasting on the Grid for the South Eastern Mediterranean Region, Earth Science Informatics, 3, 209-218.
42. F. E. Psomopoulos, P. A. Mitkas, C. S. Krinas, and I. N.Demetropoulos, “A grid-enabled algorithm yields figure-eight molecular knot,” Molecular Simulation, vol. 35, no. 9, pp. 725–736, 2009.
43. I. Kantemiris, P. Karaiskos, G. Lymperopoulou, G. Koukourakis, A. Angelopoulos, “Dose averaged LET distribution of Z=1 up to Z=6 ion beams”, Radiother. Oncol. 96, S536, 2010 9.
44. I. Kantemiris, P. Karaiskos, P. Papagiannis, A. Angelopoulos, “Dose and dose averaged LET comparison of 1H, 4He, 6Li, 8Be, 10B, 12C, 14N, and 16O ion beams forming a spread-out Bragg peak”, Med. Phys. 38 (12), 2011
45. A. Luehr, J. Toftegaard, I. Kantemiris, N. Bassler, “Stopping power: the generic library libdedx and a study of clinically relevant stopping-power ratios for different ions”, International Journal of Radiation Biology, January 2012, Vol. 88, No. 1-2 : pp. 209-212

Conferences

1. I. Maglogiannis,  A. Chatzioannou,  J. Soldatos,  V. Mylonakis,  J. Kanaris,  “An Application Platform Enabling High Performance Grid Processing of Microarray Experiments”, In Proc. 20th IEEE Conf on Computer Based Medical Systems CBMS2007 pp. 477-482, Maribor Slovenia
2. A. Chatziioannou, I. Kanaris, I. Maglogiannis, C. Doukas, P. Moulos, E. Pilalis and F. Kolisis : “GRISSOM web based Grid portal: Exploiting the power of Grid infrastructure for the interpretation and storage of DNA microarray experiments” In Proc of  9th IEEE International Special Topic Conference on Information Technology in Biomedicine (ITAB 2009) Larnaka Cyprus
3. Fotis E. Psomopoulos and Pericles A. Mitkas: “Sizing Up: Bioinformatics in a Grid Context”, 3rd Conference of the Hellenic Society For Computational Biology and Bioinformatics – HSCBB ’08, 30-31 October 2008, Thessaloniki, Greece.
4. Helen Polychroniadou, Fotis E. Psomopoulos and Pericles A. Mitkas: “g-Class: A Divide and Conquer Application for Grid Protein Classification”, Proceedings of the 2nd ADMKD 2006: Workshop on Data Mining and Knowledge Discovery (in conjunction with ADBIS’2006: The 10th East-European Conference on Advances in Databases and Information Systems), 3-7 September 2006, Thessaloniki, Greece, pp. 121-132.
5. Christos N. Gkekas, Fotis E. Psomopoulos and Pericles A. Mitkas, “A parallel data mining methodology for protein function prediction utilizing finite state automata”, presented at the 2nd Electrical and Computer Engineering Student Conference, April 2008, Athens, Greece.
6. Skarlatoudis A.A., C.B. Papazachos, P. Moczo, J. Kristek and N. Theodoulidis, (2008b). Ground motions simulations for the city of Thessaloniki, Greece, using a 3-D Finite-Difference wave propagation method, European Geosciences Union (EGU) General Assembly, Vienna, Austria and 31st European General Assembly of the European Seismological Commission, Chania, Greece.
7. D. Kalles and P. Kanellopoulos. “A Minimax Tutor for Learning to Play a Board Game”, Workshop on Artificial Intelligence in Games, a workshop of the 18th European Conference on Artificial Intelligence, 2008.
8. D. Kalles and P. Kanellopoulos. “A Pendulum Effect in Co-evolutionary Learning in Games”, European Workshop in Reinforcement Learning, 2011.
9. P. Gkonis, T. Athanaileas, G. Tsoulos and D. Kaklamani, “Performance of WCDMA in a multicellular network for different multiuser detection strategies”, Proceedings of the 7^th International Conference on Intelligent Transport Systems Telecommunications (ITST 2007), Sofia Antipolis, France, June 2007, pp. 195-200.
10. P. Gkonis, G. Tsoulos and D. Kaklamani, “An adaptive admission control strategy for WCDMA multicellular networks with non-uniform traffic”, Proceedings of the IEEE 66^th Vehicular Technical Conference (VTC 2007), Baltimore, USA, 1-3 October 2007, pp. 989-993.
11. P. K. Gkonis, G. V. Tsoulos and D. I. Kaklamani, “Performance evaluation of an adaptive sectorization strategy for WCDMA cellular networks with hotspot areas”, Proceedings of the 10^th European Conference on Wireless Technology (ECWT 2007), European Microwave Week 2007 (EuMW 2007), Munich, Germany, 8-10 October 2007, pp. 86-89.
12. P. Gkonis, G. Tsoulos and D. I. Kaklamani, “Performance of WCDMA networks with space-time block coding and multiuser detection”, Proceedings of the 2nd European Conference on Antennas and Propagation (EuCAP 2007), Edinburgh, UK, 11-16 November 2007
13. P. Gkonis, G. Tsoulos and D. I. Kaklamani, “Capacity of WCDMA multicellular networks under different radio resource management strategies”, Proceedings of the IEEE International Symposium on Wireless Pervasive Computing (ISWPC08), Santorini, Greece, 7-9 May 2008, pp. 60-64.
14. D. Karakasilis, F. Georgatos, L. Lambrinos, T. Alexopoulos, “Application of Live Video Streaming over GRID and Cloud Infrastructures“,  IEEE 11th International Conference on Computer and Information Technology (CIT), Pafos, Cyprus 2011
15. K. Christodoulopoulos, V. Gkamas, E. Varvarigos, “Delay Components of Job Processing in a Grid: Statistical Analysis and Modelling”, 3^rd International Conference on Networking and Services, ICNS, pp. 23-31, June 2007.
16. M. Oikonomakos, K. Christodoulopoulos, E. Varvarigos, Profiling Computation Jobs in Grid Systems, Proc. 7th IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2007), Rio, Brazil, May 2007, pp. 197-204.
17. A.A. Skarlatoudis  and C.B. Papazachos, “Implementation of a non-splitting formulation of perfect matching layer in a 3D – 4th order staggered-grid velocity-stress finite-difference scheme”, Bull. Geol. Soc of Greece, in the proceedings of 12th International Congress, Patras, Greece, (2010).
18. Skarlatoudis A.A., C.B. Papazachos, P. Moczo, J. Kristek, N. Theodoulidis and P. Apostolidis, (2007). Evaluation of ground motions simulations for the city of Thessaloniki, Greece using the FD method: the role of site effects and focal mechanism at short epicentral distances, European Geosciences Union (EGU) General Assembly, Vienna, Austria.
19. Tzanis, G., Berberidis, C., and Vlahavas, I. (2006). A Novel Data Mining Approach for the Accurate Prediction of Translation Initiation Sites, In Proceedings of the 7th International Symposium on Biological and Medical Data Analysis, Thessaloniki, Greece, 92-103.
20. Tzanis, G. Berberidis, C., and Vlahavas, I. (2007). MANTIS: A Data Mining Methodology for Effective Translation Initiation Site Prediction. In Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Lyon, France, 6343-6347.
21. Tzanis, G. and Vlahavas, I. (2007). Accurate Classification of SAGE Data Based on Frequent Patterns of Gene Expression. In Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2007), Patras, Greece, 96-100.
22. Tzanis, G., Kavakiotis, I., Vlahavas, I. (2008). Polyadenylation Site Prediction Using Interesting Emerging Patterns, In Proceedings of the 8th IEEE International Conference on Bioinformatics and Bioengineering, IEEE, Athens, Greece, 1-7.
23. I. Kantemiris, “Dosimetry of antiproton beam with VIP polymer Gel and MRI”, 8th Workshop on Biological Effects of Antiprotons and Recent Advances in Particle Beam Cancer Therapy, Varenna, June 13-17, 2011.

Other

1. C. Doukas, I. Maglogiannis, A. Chatziioannou, “Certification and Security Issues in Biomedical Grid Portals: The GRISSOM Case Study”, “Certification and Security in Health-Related web applications: Concepts and Solutions” IGI Press (to appear).
2.  J. Soldatos, I. Maglogiannis, A. Chatzioannou, V. Mylonakis, J. Kanaris, ‘Application Architecture for High Performance Microarray Experiments over the Hellas-Grid Infrastructure’, EGEE User Forum, Manchester, United Kingdom, May 9-11, 2007.
3. A. Chatziioanou, I. Maglogiannis, I. Kanaris, C. Doukas, E. Pilalis, P. Moulos, F. Kolisis, “GRISSOM: A Web based portal and repository for interpretation and storage of DNA microarray experiments”, presented at 4th EGEE User Forum/OGF 25 and OGF Europe’s 2nd International Event, 2-6 March 2009, Catania, Italy.
4. Christos N. Gkekas, Fotis E. Psomopoulos and Pericles A. Mitkas, “A parallel data mining application for Gene Ontology term prediction”, 3d EGEE User Forum, Polydome Conference Centre, 11-14 February 2008, Clermont-Ferrand, France.
5. T. Athanaileas, P. Gkonis, G. Tsoulos and D. Kaklamani, “An adaptive framework for WCDMA system analysis in the EGEE grid infrastructure”, Book of abstracts of the 20^th Open Grid Forum, Enabling Grids for E-Science (OGF20/EGEE), Manchester, UK, 9-11 May 2007, p. 112.
6. P. Gkonis, G.Tsoulos and D. Kaklamani, “Performance evaluation of a beam-centric adaptive admission control for WCDMA cellular networks with smart antennas”, Proceedings of the 16^th IST Mobile & Wireless Communications Summit, Budapest, Hungary, 1–5 July 2007 (5 pages).
7. Fotis E. Psomopoulos and Pericles A. Mitkas, “Data Mining in Proteomics using Grid Computing”, Handbook of Research on Computational Grid Technologies for Life Sciences, Biomedicine and Healthcare, Editor: Mario Cannataro, Laboratory of Bioinformatics, University Magna Graecia of Catanzaro, 88100 Catanzaro, Italy, 2009, (chapter 13, pp. 245-267), UK: IGI Global.
8. A.A. Skarlatoudis , Korosoglou, P., Kanellopoulos, C. and Papazachos C.B, (2008a). Interaction of a 3D finite-difference application for computing synthetic waveforms with the HellasGrid infrastructure, 1st HellasGrid User Forum, Athens, Greece, 3rd EGEE User Forum, Clermont-Ferrand, France.
9. V. Tziampazlis, ”Three-dimensional Simulations of Rotational Modes in Relativistic Stars“, M.Sc Thesis, Physics Department, Aristotle University of Thessaloniki, Supervisor: Assistant Professor Nikolaos Stergioulas
10. Kotroni V., E. Floros, K. Lagouvardos, G. Pejanovic, L. Ilic, M. Zivkovic , “Regional scale multi-model and multi-analysis ensemble weather forecasting on the Grid“, SEE-GRID-SCI User Forum 2009, 9-10 December 2009, Istanbul, pp. 35-42.
11. Floros, E., V. Kotroni, K. Lagouvardos, G. Pejanovic, L. Ilic, M. Zivkovic, “Weather multi-model and multi-analysis ensemble forecasting on the Grid”, 4^th EGEE User Forum, March 4, 2009. Catania, ITALY.
12. F. Tzima, F. E. Psomopoulos, and P. A. Mitkas, “An investigation of the effect of clustering-based initialization on learning classifier systems’ effectiveness: leveraging the grid infrastructure,” in 5th EGEE User Forum, (Uppsala, Sweden), pp. 111–112, Uppsala University, April 2010.
13. F. E. Psomopoulos and P. A. Mitkas, “Badge: Bioinformatics algorithm development for grid environments,” in Local Proceedings of PCI 2009, 13th Panhellenic Conference on Informatics, (Corfu, Greece), pp. 93–107, September 2009.
14. C. N. Gkekas, F. E. Psomopoulos, and P. A. Mitkas, “Exploiting parallel data mining processing for protein annotation,” in Proceedings of the “Students Eureka” 2008, 2nd Panhellenic Scientific Student Conference on Computer Science, (Samos, Greece), pp. 242–252, August 2008.
15. F. E. Psomopoulos, P. A. Mitkas, C. S. Krinas, and I. Demetropoulos, “G-molknot: A grid enabled systematic algorithm to produce open molecular knots,” in 1st HellasGrid User Forum, Biomed Apps, (School of Physics, Amphitheatre Aristotle of the National and Kapodistrian University of Athens, Ilissia Campus, Athens, Greece), January 2008.
16. K. Chatzidimitriou, F. E. Psomopoulos, and P. A. Mitkas, “Grid-enabled parameter initialization for high performance machine learning tasks,” in 5th EGEE User Forum, (Uppsala University, Uppsala, Sweden), pp. 113–114, April 2010.
17. C. N. Gkekas, F. E. Psomopoulos, and P. A. Mitkas, “Modeling gene ontology terms using finite state automata,” in Hellenic Bioinformatics and Medical Informatics Meeting, (Biomedical Research Foundation, Academy of Athens, Greece), October 2007.

Η πλήρης ένταξη του κόμβου του Α.Π.Θ. αναμένεται να ολοκληρωθεί ως το τέλος του έτους και θα δώσει στους ερευνητές του Α.Π.Θ. που συμμετέχουν στο πείραμα ATLAS, τη δυνατότητα άμεσης πρόσβασης σε παραγώμενα πειραματικά δεδομένα του πειράματος, ανοίγοντας έτσι νέους ορίζοντες για την ερευνητική τους δραστηριότητα.

Με τη συμπλήρωση 10 ετών από την ημερομηνία έναρξης της λειτουργίας του, ο κόμβος GR-01-AUTH, o οποίος ήταν ο πρώτος κόμβος πλεγματικής υποδομής (Grid) που λειτούργησε στην Ελλάδα, θα αποτελέσει και το πρώτο ελληνικό Tier-3 κόμβο που θα συνδεθεί στην υπολογιστική υποδομή του ATLAS.

Τo Κέντρο Επιστημονικών Υπολογιστικών Υπηρεσιών (K.E.Y.Y.) Α.Π.Θ παρέχει υπολογιστικές υπηρεσίες υψηλών επιδόσεων προς την ερευνητική κοινότητα του Α.Π.Θ. ενώ παράλληλα συμμετέχει ενεργά σε εθνικές και διεθνείς δράσεις σχεδιασμού και υλοποίησης κατανεμημένων & υπερυπολογιστικών υποδομών για την ακαδημαϊκή/ερευνητική κοινότητα.

Πληροφορίες:

Ομάδα Φυσικής Υψηλών Ενεργειών – ATLAS
email: Chariclia.Petridou@cern.ch
www: http://www.physics.auth.gr/atlas/

Κέντρο Επιστημονικών Υπολογιστικών Υπηρεσιών Α.Π.Θ.
e-mail: contact@grid.auth.gr
www: http://www.grid.auth.gr

In the area of bioinformatics, research efforts are drawn to whole genome functional genomics studies, through the use of DNA microarray high-throughput technology, that is the standard experimental technique over the last six years for the study of whole gene in an organism. Experiments of this type however, have a very high potential impact and are therefore adopted by a large and ever increasing number of laboratories. Laboratories and research groups are nowadays starving for powerful computational methodologies for processing microarray datasets, supporting through friendly interfacing functionalities related to array fabrication, labeling, hybridization, and data analysis. Such tools are a key prerequisite for effective data analysis that could automate and ultimately provide new insights for the transcriptomic component of the biological systems investigated.

The problem that arises with the ever growing usage of the microarray technology is the large amount of data produced everyday and the drawback of data preprocessing and statistical selection algorithms that demand high computational resources, making single threaded applications time consuming.

By utilizing the grid infrastructure, HECTOR [1] team managed to overcome the computational burden of such single node applications. The starting point was a set of legacy MATLAB applications, the ANDROMEDA (Automated aND Robust Microarray Experiments Data Analysis) [2], originally developed by researchers of the National Hellenic Research Foundation (NHRF), a new parallel statistical analysis pipeline was created.

The initial step was the transformation of all needed MATLAB functions to an open source derivative, OctaveForge that is installed on HellasGrid nodes in order to perform the data analysis. Accordingly all the initial data parsing functions were implemented from scratch in Python scripts to further speed up the operation. The pipeline was separated in different phases of parsing-preprocessing, normalization, statistical analysis and post-processing and was studied for potential parallelization. Due to the large computational needs and the ability to run independently from each other, the first two phases were designed to run on multiple nodes as seen in Figure 1. The use of MPI technology provided the ability to implement the whole workflow of the application and orchestrate the execution on multiple nodes of a site and monitor their time and possible errors. After the completion of the parallelized part, data are sent to the head MPI node for further statistical processing that need the existence of all experiment related data.

In HECTOR platform all this processing workflow is fully automated and provides a JSP based user friendly web portal that enables scientists/users from the fields of biology and medicine to use the processing power of grid without any specialty on informatics science. After the completion of statistical analysis, users are notified to get their resulting list and to further annotate their experiments with the use of MIAME XML platform implemented, and to decide whether they want to make the results public or not through the distributed database of HECTOR platform that supports the HellasGrid  storage elements.

Figure 1 - HECTOR platform

Contacts

• I. Maglogiannis, University of the Aegean, Samos, Greece, imaglo (at) aegean.gr
• A. Chatziioannou, National Hellenic Research Foundation, Greece, achatzi (at) eie.gr
• I . Kanaris, University of the Aegean, Samos, Greece, kanaris.i (at) aegean.gr
• V. Mylonakis, National Technical University of Athens, Athens, Greece, vmil (at) netmode.ntua.gr
• J. Soldatos, Athens Information Technology, Athens, Greece, jsol (at) ait.edu.gr

References

1. I. Maglogiannis,  A. Chatzioannou,  J. Soldatos,  V. Mylonakis,  J. Kanaris,  “An Application Platform Enabling High Performance Grid Processing of Microarray Experiments”, In Proc. 20th IEEE Conf on Computer Based Medical Systems CBMS2007 pp. 477-482, Maribor Slovenia
2. J. Soldatos, I. Maglogiannis, A. Chatzioannou, V. Mylonakis, J. Kanaris, ‘Application Architecture for High Performance Microarray Experiments over the Hellas-Grid Infrastructure’, EGEE User Forum, Manchester, United Kingdom, May 9-11, 2007.
3. Kanaris, V. Mylonakis, A. Chatziioannou, I. Maglogiannis, and J.Soldatos, “HECTOR: Enabling Microarray Experiments over the Hellenic Grid Infrastructure,” J. Grid Comput., vol. 7, no. 3, pp. 1–22, Aug. 20

Transcriptomic experiments perform global gene expression monitoring, enabling thus, thorough probing of the in-vivo cellular state and its regulation, in healthy and disease state, in response to numerous environmental stimuli, across different species, etc. DNA microarrays have become a mainstay for a vast range of genomic applications, helping to identify significant alterations in transcriptomic expression of the system investigated, and map them to specific phenotypic outcomes.

There is a pressing request for computationally intelligent solutions, which manage to provide versatile, powerful and user-friendly data mining functionalities, in order to tackle the enormous underlying complexity of gene profiling experiments. On the other hand, there is an ever growing need for computational power as the size of the experimental datasets keeps increasing.

In Figure 1, an overview of the workflow structure of GRISSOM [1] is illustrated. The platform has been designed in order to effectively accommodate the needs of a wide range of users with different levels of expertise, aspiring to perform versatile and varying series of operations. The core of the developed web application, namely the quantitative signal processing and statistical analysis of the microarrays, which represent the computationally expensive part of the analysis pipeline, but also the storage of the datasets as well as of the annotation files, are exploiting the HellasGrid infrastructure. Overall, the DNA microarray experimental data analysis tasks implemented within the platform, encompass diversified processing steps, entailing versatile, heterogeneous in nature of processing, data type and complexity tasks. These can be basically partitioned into the categories of data import, gene selection, gene annotation tasks (gene, platform, and experiment), integrative interpretation capabilities, secure database storage and maintenance, and support of various output formats.

Figure 1- Workflow structure of GRISSOM

With respect to the efficient interpretation of DNA microarray experiments, GRISSOM supports gene classification based on clustering algorithms or cellular pathway analysis, through the integration of statistical ranking of annotated genomic experimental results. In this way, statistical enrichment analysis is performed, which exploits controlled biological vocabularies like the GO or the KEGG Ontology. Another capability of GRISSOM is the reconstruction of cellular network super-pathway models, which are SBML-compliant by exploiting KEGGConverter that is based on the KEGG pathway IDs derived from the analysis performed by StRAnGER.

Grids represent extremely heterogeneous, in terms of resources, tasks, policies and time demands, environments, posing sheer challenges regarding the effective accommodation and routing of all these striving requests. In order for GRISSOM being able to use as much processing power as possible, and keep manageable the queuing time too, the application has been designed to enable distributed computing methodologies, for two different grid configurations: a) through job schedulers that perform supervised job management in the Grid, as derived by a special directed acyclic graph (DAG), written in Python and Octave Forge mathematical language and b) by utilizing MPI computing workflows. DAG management renders the system resilient even for huge datasets, which can be executed even when the grid infrastructure is extremely loaded and has minimal resource availability. An Overview of how the web application resides between users and the HellesGRID infrastructure is shown in Figure 2.

Figure 2 - Overview of how the web application resides between users and the HellasGrid infrastructure

Contacts

• I. Maglogiannis, University of Central Greece, Lamia, Greece, imaglo (at) ucg.gr
• A. Chatziioannou, National Hellenic Research Foundation, Greece, achatzi (at) eie.gr
• I. Kanaris, University of the Aegean, Mytilene, Greece, kanaris.i (at) aegean.gr
• C. Doukas, University of the Aegean, Mytilene, Greece, doukas (at) aegean.gr
• P. Moulos, National Hellenic Research Foundation, Greece, p.moulos (at) eie.gr
• F. Kolisis, National Hellenic Research Foundation, Greece, kolisis (at) eie.gr

References

1. GRISSOM Platform: Enabling distributed Processing and Management of Biological Data through fusion of Grid and Web Technologies. A. Chatziioannou, I. Kanaris, C. Doukas, P. Moulos, F.N. Kolisis and I. Maglogiannis (IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, 2011 15 (1), art. no. 5638146, pp. 83-92.
2. A. Chatziioannou, I. Kanaris, I. Maglogiannis, C. Doukas, P. Moulos, E. Pilalis and F. Kolisis : “GRISSOM web based Grid portal: Exploiting the power of Grid infrastructure for the interpretation and storage of DNA microarray experiments” In Proc of  9th IEEE International Special Topic Conference on Information Technology in Biomedicine (ITAB 2009) Larnaka Cyprus
3. KEGGconverter: a tool for the in-silico modelling of metabolic networks of the KEGG Pathways database. K.Moutselos, I.Kanaris, A.Chatziioannou, I. Maglogiannis, F.N. Kolisis (BMC Bioinformatics 10:324), 2009. (featured article of the volume, characterized as Highly Accessed).
4. C. Doukas, I. Maglogiannis, A. Chatziioannou, “Certification and Security Issues in Biomedical Grid Portals: The GRISSOM Case Study”, “Certification and Security in Health-Related web applications: Concepts and Solutions” IGI Press (to appear)
5. A. Chatziioanou, I. Maglogiannis, I. Kanaris, C. Doukas, E. Pilalis, P. Moulos, F. Kolisis, “GRISSOM: A Web based portal and repository for interpretation and storage of DNA microarray experiments”, presented at 4th EGEE User Forum/OGF 25 and OGF Europe’s 2nd International Event, 2-6 March 2009, Catania, Italy.

Στόχος του φόρουμ είναι τόσο η ανάδειξη των ερευνητικών δραστηριοτήτων των χρηστών της υποδομής EGI όσο και η ενημέρωση συνολικά της κοινότητας χρηστών περί των τελευταίων εξελίξεων σε εργαλεία, υπηρεσίες και διαθέσιμες τεχνολογίες. Οι διοργανωτές του φόρουμ προσκαλλούν όσα μέλη της κοινότητας επιθυμούν να παρουσιάσουν την ερευνητική τους δραστηριότητα και το πώς επωφελήθηκαν από την υποδομή EGI και τις παρεχόμενες υπηρεσίες, να υποβάλλουν περίληψη (abstract) της εργασίας τους μέχρι τις 2 Δεκεμβρίου 2011. Περισσότερες πληροφορίες μπορούν να βρούν οι ενδιαφερόμενοι εδώ.

Halogenated organic compounds are being effectively utilized as industrial solvents, fire-suppressants and refrigeration media for several decades, the first and best known class being ChloroFluoroCarbons (CFC). However, CFCs are harmful to the protective layer of stratospheric ozone, attributed to the presence of chlorine atoms. Thus, new classes of CFC alternatives containing no chlorine atoms are appropriately devised, which must additionally possess short atmospheric lifetimes and a minimal contribution to global warming. An extensive investigation of their reactivity is primarily performed by experimental studies, in order to assess their impact in atmospheric quality. Since experiments may not be able to provide all the answers, assistance is addressed to molecular quantum mechanical calculations.

The atmospheric reactivity of two hydrofluoro-olefins (HFOs), CF₃CF=CH2 (2,3,3,3-tetrafluoropropene, HFO-1234yf) and (Z)-CF₃CF=CHF (1,2,3,3,3-pentafluoropropene, HFO-1225ye), was investigated both experimentally and theoretically. Theoretical calculations were performed by Density Functional Theory (DFT) in order to elucidate several aspects of HFOs chemistry in the atmosphere.

Theoretical calculations yielded equilibrium molecular structures along with vibrational frequencies and the absolute electronic energies at reliable levels of theory comprising of the B3P86 functional with the aug-cc-pVDZ and aug-cc-pVTZ basis sets, respectively. The data were subsequently fed into statistical thermodynamics formulas to compute the reaction enthalpies. The most likely sites for the Cl, OH and NO₃ addition to the double bond of each HFO were determined, and the energetics of the initial adduct formation pathways helped to explain the experimentally observed pressure dependence difference between Cl and OH reactions. Furthermore, the calculated exothermicities for the degradation reactions of peroxy radicals in the presence of O₂, NO and HO₂ yielded the most likely atmospheric degradation products of HFOs, in support of the experimental results.

Figure 1 - Enthalpy diagram (298.15 K) for the reaction of Cl, NO3, and OH with CF3CF=CH2 and formation of peroxy adducts calculated at the B3P86/aug-cc-pVTZ level of theory.

As, a large amount of calculations was needed, due to the number of molecular species involved in this work, the computing power of the South-Eastern European Virtual Organization (SEE-VO) was effectively utilized to accomplish the task. Automation of the job submission and data retrieval processes was performed by shell scripts using commands of the gLite middleware.

Future plans include the examination of the degradation mechanism of halogenated organic molecules as harmful and toxic pollutants in aqueous  environments is currently being investigated using Density Functional Theory (DFT).

Contacts

• Dr. Yannis G. Lazarou, Institute of Physical Chemistry, NCSR “Demokritos”, Aghia Paraskevi, Attiki, Greece, lazarou (at) chem.demokritos.gr
• Dr. Vassileios C. Papadimitriou, Department of Chemistry, University of Crete, Heraklion, Greece, bpapadim (at) chemistry.uoc.gr
• Dr. James B. Burkholder, ESRL, CSD, National Oceanic and Atmospheric Administration (NOAA), Boulder, Colorado, USA, James.B.Burkholder (at) noaa.gov
• Dr. Ranajit K. Talukdar, CIRES, University of Colorado, Boulder, Colorado, USA, Ranajit.K.Talukdar (at) noaa.gov

Figure 1: Achlioptas Process according to the sum rule (APSR) for site percolation. White cells correspond to unoccupied sites while colored cells correspond to occupied sites. Different colors (red,green,gray,blue) indicate different clusters. (a) We randomly select two trial unoccupied sites (yellow), noted by A and B, one at a time. We evaluate the size of the clusters that are formed and contain sites A and B, \(s_A\) and \(s_B\) respectively. In this example \(s_A = 10\) and \(s_B = 14\). (b) According to the Achlioptas Process, we keep site A which leads to the smaller cluster and discard site B.

Following the first publication of Achlioptas et al., a debate was initiated between various teams whether the procedure is continuous or discontinuous. Contributing to these considerations, we have investigated explosive site percolation, both using the product and sum rules. It was found that the exponent \(\beta / \nu \) is vanishing small for both cases, pointing towards the continuity of the transition. Also, we performed numerical analysis for the case of a reverse Achlioptas process (Figure 2). It was shown that for finite systems there is a hysteresis loop between the reverse and forward procedure (Figure 2). This loop vanishes at infinity, giving strong evidence for the continuity of the “explosive” site percolation (Figure 3). Moreover, “explosive” site and bond percolation seem to belong to a different universality class

		Figure 2: Reverse Achlioptas Process (AP1) for site percolation according to the sum rule. Blue is for the occupied sites while white for the unoccupied sites. Initially, the lattice is fully occupied. (a) An instance of the process. We randomly choose two trial sites (yellow), noted as A and B, and remove them from the lattice. (b) The clusters formed after the removal. (c) We place site A again in the lattice and calculate the size of the cluster in which it belongs, \(s_A = 16\). (d) We do the same as before for the case of site B and calculate \(s_B = 26\). We remove site A which leads to the formation of the smaller cluster and keep site B.

Figure 3: (a) Hysteresis loop between a reverse (red dots) and the forward (black squares) Achlioptas process for a \(700\times 700\) system. (b) The loop vanishes in the thermodynamic limit

Simulations were performed on the EGI. A diagram of the number of jobs and CPU hours consumed per month is shown in Figure 4. We have used extensively the gLite parametric job submission mechanism, using as parameter the different realizations of the system. On average, more than 1000 jobs per simulation were submitted for each lattice size. Considering a typical \( 1000 \times 1000 \) lattice, the average time consumed for one run approached 172 minutes. If we had to perform the calculations on a single CPU, this would mean that it would take us 120 days to get complete results for just one lattice size. Using the EGI thus has helped us minimize this time approximately to 172 minutes. This translates to a time gain of the order of \(10^3\). Moreover, given the availability of more resources, this gain may be even higher. This is a very important feature, because we can numerically analyze systems of the order of \(10^6\) in a tolerable amount of time.

Figure 4: Number of jobs and CPU hours per month consumed for the simulations

References:

1. D.Achlioptas, R.M. D’Souza and J. Spencer, Explosive Percolation in Random Networks, Science 323,p. 1453 ,(2009)
2. R.A. da Costa, S.N.Dorogovtsev, A.V.Goltsev, and J.F.F.Mendes, “Explosive Percolation” Transition is Actually Continuous, Physical Review Letters 105(25),255701,(2010)
3. P.Grassberger, C. Christensen, G. Bizhani, S-W Son, and M. Paczuski, Explosive Percolation is Continuous, but with Unusual Finite Size Behavior, Phys Rev Lett. 106(22), (2011)
4. O. Riordan and L. Warnke, Explosive percolation is continuous, Science 333 (2011)
5. R.M. Ziff, Explosive growth in biased dynamic percolation on two-dimensional regular lattice networks, Physical Review Letters 103(4),45701,(2009)
6. F. Radicchi and S. Fortunato, Explosive Percolation: A numerical analysis, Physical Review E 81(3),036110,(2010)
7. N.A.M. Araújo and H.J.Hermann, Explosive Percolation via Control of the Largest Cluster, Physical Review Letters 105(3),035701,(2010)

The employment of traditional and emerging materials in technological applications presupposes a detailed knowledge of their physical properties. An accurate description of these properties at the atomic-scale is linked to the solution of quantum-mechanical (QM) differential equations, a task of immense challenges due to interactions among electrons in extended systems. DFT codes such as VASP utilize one of the most popular theoretical approaches, the so-called density-functional theory, to solve the QM equations and describe, thus, the electronic, chemical, mechanical, optical, or transport properties of a plethora of physical systems (bulk solids, surfaces, nano-systems of different dimensionality, molecules, etc).

Figure 1 - Energy variation during diffusion of a C dopant in TiO2

The scientists from the Computational Materials Science Group have undertaken a number of investigations [1-8] using the HellasGrid infrastructure to perform DFT calculations. These studies probed the properties of materials that have attracted strong interest in recent years. Examples include TiO₂, a material with potential use in photovoltaics and photocatalysis,[1], [2] organic semiconductors,[3], [6]  layered hard systems, [4], [5] and materials employed in novel electronic devices. [7], [8].  The associated publications [1], [8] provide extensive information on how the detailed knowledge of the QM properties of the above systems can lead to further optimization of related applications.

As VASP is a parallel code that utilizes MPI to distribute the workload to different nodes, first-principles calculations are computationally intensive and the use of a large network of clusters like the HellasGrid is indispensable for this type of studies. The number of nodes required varies from problem to problem, with small jobs typically running on 4-8 nodes, to larger tasks of 32 or more nodes.

DFT calculations are currently underway to probe the properties of several other important materials, such as graphene and other two-dimensional materials, carbon nanotubes, silicon nanowires, organic photovoltaics, and others. The completion of these studies will lead to new publications and boost the scientists’ understanding for systems that play a key role in emerging technologies.

Contacts

• Leonidas Tsetseris, Assistant Professor, NTUA, leont (at) mail.ntua.gr
• Georgios Volonakis, PhD candidate, AUTH, gvolo (at) physics.auth.gr
• Evangelos Golias, PhD candidate, NTUA, vgolias (at) ims.demokritos.gr

References

1. “Stability and dynamics of carbon and nitrogen dopants in anatase TiO₂”, L. Tsetseris, Physical Review B 81, 165205 (2010).
2. “Configurations, electronic properties, and diffusion of carbon and nitrogen dopants in rutile TiO₂”, L. Tsetseris, Physical Review B 84, 165201 (2011).
3. “Stability of Group-V Endohedral Fullerenes”, L. Tsetseris, Journal of Physical Chemistry C 115, 3528 (2011).
4. “Electronic and structural properties of TiB₂: Bulk, surface, and nanoscale effects”, G. Volonakis, L. Tsetseris, and S. Logothetidis, Materials Science and Engineering B 176, 484 (2011).
5. “Excess of boron in TiB₂ superhard thin films: a combined experimental and ab initio study”, N. Kalfagiannis, G. Volonakis, L. Tsetseris, and S. Logothetidis, Journal of Physics D 44, 385402 (2011).
6. “Impurity-related vibrational modes in a pentacene crystal”, G. Volonakis, L. Tsetseris, and S. Logothetidis, European Physical Journal: Applied Physics 55, 23903 (2011).
7. “Ge volatilization products in high-k gate dielectrics”, E. Golias, L. Tsetseris, A. Dimoulas, and S. T. Pantelides, Microelectronic Engineering 88, 427 (2011).
8. “Ge-related impurities in high-k oxides: Carrier traps and interaction with native defects”, E. Golias, L. Tsetseris, and A. Dimoulas, Microelectronic Engineering 88, 1432 (2011).

In order to have a good estimation of performance and scaling up to some thousands cores of these applications, well equilibrated initial configurations are necessary. For each package a number of physical systems were prepared. The prepared configurations cover various simulation system sizes and methods. Each of these configurations was equilibrated using grid infrastructure. A minimal run of 10³ steps for each case of Gromacs and NAMD (in order to have reproducible performance and scaling) takes about 10-30 minutes using 1024-8192 cores on Tier-0 systems (170-4096 core hours). A typical equilibration step needs, depending on the system, more than 10⁵ steps. For Cp2k, much more time is necessary to obtain equilibrated initial configuration (each step for large cases takes ~2 hours using 2048 cores on BG/P machine at Juelich).

In all the aforementioned cases, the equilibration runs were performed using a number of save/restart jobs on Hellasgrid infrastructure, using from 8 up to 32 cores for each job. These applications, and few more (Quantum Espresso, Towhee), were ported and optimized to run on the existing Hellasgrid architectures. As far as Gromacs concerns, it has an internal CPU detection mechanism, hence the corresponding executable is internally optimized for different architectures. For NAMD and Cp2k cases respectively, different executables were produced taking into account the existing hardware. Currently, two major versions of executables were produced: one that runs on all Hellasgrid sites, with optimization for CPUs that have SSE2 instructions and one for CPUs that have SSE4.1 instructions (HG-06). In all cases openmpi-1.4.3 was used as parallel environment. For NAMD and Cp2k, the free unsupported version of Intel compilers was used, obtaining an additional ~2x performance boost. In order to avoid installation of Intel compilers suite in the Hellasgrid clusters, static Intel libraries linking was used. Additional math libraries were required by these packages (fftw2, fftw3, gsl, lapack,  Atlas, libint, Blacs, Scalapack). These libraries were compiled and installed on UI (Users Interface service) machine(s). Static linking was followed in every case, thus installation on WNs (Working Nodes) was not necessary.

The HellasGrid Infrastructure provided the CPU resources needed to proceed with the preparation of the equilibrated initial configurations. These packages will be available, for use on Virtual Organization basis, on Hellasgrid clusters soon (this is a work in progress).

Contact details:

• Marios Chatziangelou, IASA, mhaggel (at) iasa.gr
• Dimitris Dellis IASA, ntell (at) iasa.gr
• HellasGrid Application Support Team, IASA, application-support (at) hellasgrid.gr

The strong correlation that exists between the properties of a protein and its motif sequence (Figure 1) makes the prediction of protein function possible. The core concept of any approach is to employ data mining techniques in order to construct models, based on data generated from already annotated protein sequences. A major issue in such approaches is the complexity of the problem in terms of data size and computational cost. However, the utilization of the HellasGrid Infrastructure and the EGI Grid, coupled with the close support of the Scientific Computing Center at A.U.Th., helped overcome the computational difficulties often encountered in protein classification problems.

Figure 1: [a] P00747 (Plasminogen precursor – PLMN_HUMAN) protein chain, and [b] an amino-acid pattern expressed as a regular expression

G-Class was the first data-mining algorithm successfully ported to the EGI Grid infrastructure [4]. The G-Class methodology follows a “divide and conquer” approach comprised of 3 steps (Figure 2).

Figure 2: First, protein data from PROSITE, an expert-based database, are divided into multiple disjoint sets, each one preserving the original data distribution. The new sets are used as training sets, and multiple models are derived by means of standard data mining algorithms. Finally, the models are combined to produce the final classification rules, which can be used to classify a given instance and evaluate the methodology.

G-Class was a fairly simplistic approach to the protein classification problem, using generic data mining algorithms for the construction of several models simultaneously. However, the results were impressive both in terms of the speed-up ratio (ranging from 10 to 60) and the amount of data (ranging from 662 proteins over 27 different classes, to 7027 proteins over 96 classes) that were able to be processed (Figure 3).

Figure 3: The processing time in all cases follows the \(e^{-\alpha x}\) model, where \(\alpha\) depends on the size of the original dataset and \(x\) is the number of splits. The accuracy of the methodology is fairly constant over the number of splits, with minor fluctuations owing to the distribution of the instances of the overlapping protein classes over the different dataset splits.

A second approach was aiming towards the automatic annotation of protein sequences. Although there are a lot of tools for protein annotation, such as the Gene Ontology Project, ProDom, Pfam, and SCOP, in order to assign annotation terms to new non-annotated protein sequences, they have to be either processed directly in a lab or characterized through similarity to already annotated sequences. At the moment, the amino acid sequence of more than 1.000.000 proteins has been obtained. On the contrary, the properties and functions of only 4% of these proteins are known. Therefore, the need for a systematic way to derive clues for the properties of a protein by inspecting its amino acid sequence is obvious. PROTEAS is a novel parallel methodology for protein function prediction which predicts the annotation of an unknown protein, by running its motif sequence each model, producing similarity scores [5-6]. This methodology has been implemented so that it can effectively utilize various classification schemata, such as Gene Ontology, SCOP families, etc (Figure 4).

Figure 4: PROTEAS workflow diagram

The main drawback of this methodology is that it requires a substantial amount of computational time to complete. It has been shown experimentally that the execution time needed to process the entire dataset on a single processor is prohibitively long. In order to address this issue, PROTEAS has been implemented both as a standalone and as a grid-based application. The grid-based application utilizes the MPI library for communication between distinct processes and uses the EGI Grid infrastructure in order to minimize the execution times (Figure 5).

Figure 5: Execution times for model training

Moreover, the Grid provides for the seamless integration of the training process and the actual model evaluation by allowing the concurrent retraining of Gene Ontology models from different input sources or experts and the use of the existing ones (Figure 6).

Figure 6: Execution times for specific Train/Test set ratio and different number of input files (left column), and for different ratios but specific number of input files (right column)

The application was executed on available clusters using from 4 to 16 processors in various experiment configurations (Figure 7). In all cases the accuracy of the results was very high and the overall execution time was satisfactory.

Figure 7: Total processing times for the classification of a single protein sequence, based on the number of CPUs used and the number of input files used as the model construction base.

Contact details:

• Pericles A. Mitkas, Professor, AUTH, mitkas (at) eng.auth.gr
• Fotis E. Psomopoulos, Research Associate, CERTH, fpsom (at) issel.ee.auth.gr
• Scientific Computing Center, AUTH, contact (at) grid.auth.gr

References:

1. Fotis E. Psomopoulos and Pericles A. Mitkas, “Bioinformatics Algorithm Development for Grid Environments”, Journal of Systems & Software, vol. 83, No 7. (2010), pp. 1249-1257.
2. Fotis E. Psomopoulos and Pericles A. Mitkas, “Data Mining in Proteomics using Grid Computing”, Handbook of Research on Computational Grid Technologies for Life Sciences, Biomedicine and Healthcare, Editor: Mario Cannataro, Laboratory of Bioinformatics, University Magna Graecia of Catanzaro, 88100 Catanzaro, Italy, 2009, (chapter 13, pp. 245-267), UK: IGI Global.
3. Fotis E. Psomopoulos and Pericles A. Mitkas: “Sizing Up: Bioinformatics in a Grid Context”, 3rd Conference of the Hellenic Society For Computational Biology and Bioinformatics – HSCBB ’08, 30-31 October 2008, Thessaloniki, Greece.
4. Helen Polychroniadou, Fotis E. Psomopoulos and Pericles A. Mitkas: “g-Class: A Divide and Conquer Application for Grid Protein Classification”, Proceedings of the 2nd ADMKD 2006: Workshop on Data Mining and Knowledge Discovery (in conjunction with ADBIS’2006: The 10th East-European Conference on Advances in Databases and Information Systems), 3-7 September 2006, Thessaloniki, Greece, pp. 121-132.
5. Christos N. Gkekas, Fotis E. Psomopoulos and Pericles A. Mitkas, “A parallel data mining application for Gene Ontology term prediction”, 3d EGEE User Forum, Polydome Conference Centre, 11-14 February 2008, Clermont-Ferrand, France.
6. Christos N. Gkekas, Fotis E. Psomopoulos and Pericles A. Mitkas, “A parallel data mining methodology for protein function prediction utilizing finite state automata”, presented at the 2nd Electrical and Computer Engineering Student Conference, April 2008, Athens, Greece.

The city of Thessaloniki (Northern Greece) was selected since it is located in a moderate-to-high seismicity region (Papazachos et al., 1983), with the Servomacedonian massif and Northern Aegean through areas exhibiting the highest seismicity (Figure 1). The city suffered several large earthquakes throughout its history, many of them causing significant damages and human losses (Papazachos and Papazachou, 2002).

Figure 1: Map of known earthquakes with M≥3.0 which occurred in the broader area of central–northern Greece from the historical times (550 BC) till 2007 (Figure after Skarlatoudis et al., 2011a).

An explicit 3D 4th-order velocity-stress finite-difference scheme with discontinuous spatial grid was used to produce synthetic waveforms with numerical simulations. The scheme solves the equation of motion and Hooke’s law for viscoelastic medium with rheology described by the generalized Maxwell body model. Details on the scheme, its grid and material parameterization are provided by Moczo et al. (2002), Kristek & Moczo (2003), Moczo & Kristek (2005), Moczo et al. (2007) and Kristek et al. (2009b).

The computational model used for the simulations is based on the geophysical-geotechnical model and the dynamic characteristics of the soil formations proposed by Anastasiadis et al. (2001) and covers an area of 22 x 16 Km2 (dotted rectangle in Figure 1) (Skarlatoudis et al., 2007; 2008b; Skarlatoudis et al., 2010).

Numerical simulations were performed for six seismic scenarios, corresponding to three different hypocentral locations and two different focal mechanisms for each one. Seismic scenarios with E-W trending normal faults are referred as scenarios (a), while the ones with NW-SE trending normal faults as scenarios (b) (Figure 2). Both types of normal faults (E-W and NW-SE) are the dominant types of faults in the vicinity of the broader Thessaloniki area (e.g. Vamvakaris et al., 2006). Synthetic waveforms were produced for a coarse grid of receivers, in order to study the spatial variation of site-effects on seismic motion in the broader metropolitan area of Thessaloniki (Figure 2).

Figure 2: Earthquake locations used for the examined seismic scenarios (red stars) and the focal mechanisms used for each scenario. The coarser grid of receivers used for studying the spatial variation of various waveform and site-effect parameters for the six earthquake scenarios is also shown (black diamonds). The location of site OBS, used as a reference station in computations, is denoted with a yellow triangle (Figure after Skarlatoudis et al., 2011a).

The application that implements the 3DFD method is using the MPI libraries for inter-process communications, namely the mpich2 implementation. The compilation and execution of the code was tested in different types of machines and different Fortran90 compilers (commercial and free). The most accurate results and the minimum execution time in each system were achieved with the use of the commercial compiler Pathscale (version 3.0) (Skarlatoudis et al., 2008a). The execution of the 3DFD code is demanding in terms both of CPU power and computer memory. For the aforementioned computational model the memory demands reached 20 GB and the time of computation (per model) was approximately 15 on the HellasGrid Infrastructure with the synchronous usage of 40 Intel Xeon processors.

The implemented workflow relies mainly on gLite middleware (Figure 3). Also a large number of test runs for checking the compatibility of the results on the Grid with the ones obtained from other computational infrastructures have been performed. Moreover the scaling of the execution of the code on the HellasGrid Infrastructure was examined (Skarlatoudis et al., 2008a).

Figure 3: Schematic representation of the workflow in HellasGrid infrastructure (Figure after Skarlatoudis et al., 2008a)

Various measures, estimated from the 3D synthetic waveforms that can provide a more detailed evaluation of site-effects, such as spectral ratios, Peak Ground Velocity (PGV), cumulative kinetic energy and Housner Intensity, were used to probe the site-effects spatial distribution and ground motion variability. In Figure 4 the Peak Ground Velocity (PGV) ratio is shown for the 3D over the corresponding 1D bedrock reference model [(PGV3D)/(PGV1D)], estimated for the coarser grid of receivers and for the two horizontal components of ground motion, for all scenarios studied (Skarlatoudis et al. 2011a). The observed relative PGV distribution from the six scenarios, exhibits high values along the coastal zone, with the highest value (~4) shown in the area near the city harbor for the E-W component. High values of relative PGV are also observed in the western parts of the model for the E-W component.

Figure 4: Spatial variation of the average, from the six seismic scenarios, ratio [(PGV3D/PGV1D)], for the horizontal components of ground motion (Figure after Skarlatoudis et al., 2011a)

The 3D wave propagation characteristics of the 4th July, 1978 aftershock (M5.1) of the 20th June, 1978 strong mainshock (M6.5) that struck the city of Thessaloniki were also studied using the 3D finite-difference approach. In Figure 5 the spatial distribution of damages in the metropolitan area of Thessaloniki after the 1978 mainshock is presented (left figure) (Leventakis, 2003), together with the corresponding distribution of the RotD50 ground motion measure of the (PGV3D)/(PGV1D) ratio, for the frequency band 0.2Hz-3Hz (Skarlatoudis et al., 2011b). According to Leventakis, (2003) the largest damage was recorded in the city harbor area and parts of the eastern area of the Thessaloniki. Despite the various limitations of the comparison, a quite good correlation is observed between the damage distribution and the PGV spatial variation, suggesting that the role of local site amplifications studies here is much more important than other factors (e.g. differences in source radiation pattern, non-linearity, etc.).

Figure 5: (Left) Spatial distribution of damage distribution in Thessaloniki caused by the mainshock of July 1978 according to Leventakis (2003). (Right) Spatial distribution of the RotD50 measure of relative PGV values (amplifications) from filtered (0.2Hz-3Hz) horizontal components (Figure after Skarlatoudis et al., 2011b).

This work has been partly performed in the framework of PENED-2003 (measure 8.3, action 8.3.4 of the 3rd EU Support Programme) and the Greek-Slovak Cooperation Agreement (EPAN 2004-2006). Most of the computations were realized on the EGI and HellasGrid infrastructureσ with the support of the Scientific Computing Center at the Aristotle University of Thessaloniki (AUTH). A significant part of the results presented here have been published in peer-review journals (see inline references) and/or presented in national and international conferences (see references at the end of this document).

Contact details:

• Papazachos C.B., Professor, AUTH, kpapaza (at) geo.auth.gr
• Skarlatoudis A.A, Dr. Seismologist, AUTH, askarlat (at) geo.auth.gr
• Scientific Computing Center, AUTH, contact (at) grid.auth.gr

References:

1. Papazachos, B. C., Tsapanos, T. M. and Panagiotopoulos, D., (1983). The time, magnitude and space distribution of the 1978 Thessaloniki seismic sequence. The Thessaloniki northern Greece earthquake of June 20, 1978 and its seismic sequence. Technical chamber of Greece, section of central Macedonia, 117-131, 1983.
2. Skarlatoudis A.A., C.B. Papazachos, P. Moczo, J. Kristek, N. Theodoulidis and P. Apostolidis, (2007). Evaluation of ground motions simulations for the city of Thessaloniki, Greece using the FD method: the role of site effects and focal mechanism at short epicentral distances, European Geosciences Union (EGU) General Assembly, Vienna, Austria.
3. Skarlatoudis A.A., Korosoglou, P., Kanellopoulos, C. and Papazachos C.B, (2008a). Interaction of a 3D finite-difference application for computing synthetic waveforms with the HellasGrid infrastructure, 1st HellasGrid User Forum, Athens, Greece, 3rd EGEE User Forum, Clermont-Ferrand, France.
4. Skarlatoudis A.A., C.B. Papazachos, P. Moczo, J. Kristek and N. Theodoulidis, (2008b). Ground motions simulations for the city of Thessaloniki, Greece, using a 3-D Finite-Difference wave propagation method, European Geosciences Union (EGU) General Assembly, Vienna, Austria and 31st European General Assembly of the European Seismological Commission, Chania, Greece.
5. Skarlatoudis A.A., C.B. Papazachos and N. Theodoulidis, (2011b). Site response study of the city of Thessaloniki (N. Greece), for the 04/07/1978 (M5.1) aftershock, using a 3D Finite-Difference wave propagation method, accepted for publication in Bull. Seism. Soc. Am.

More specific, the scientists made studies on dynamics and spectroscopy of proteins and other biomolecules, enzymatic reactions, and free energy hypersurface calculations for protein – ligand interactions by integrating very long time trajectories and making extended sampling of the classical phase space [1-3].

For problems which exhibit ergodicity in a submanifold of the phase space manifold of the system, time averages are converted to phase space averages, and thus to high throughput problems. The algorithm that have been developed is based on the principle to run short jobs, store intermediate results, and resubmit the jobs as many times as needed to achieve a predetermined total integration time [1]. This approach cures shortcomings of the current productive Grid.

The production of a free energy hypersurface, requires one to run thousands or even millions of jobs, a task which can be fulfilled if thousands of CPUs are available in a reasonable amount of time [2,3]. Scripts based on the gLite middleware have been developed and tested at the HellasGrid and EGI infrastructure which can handle such tasks.

Quantum molecular dynamics for intermediate size biomolecules require a interoperability of high throughput and high performance computing. This is scientists’ plan for solving problems involving proton-coupled electron transfer (PCET), which are encountered in enzyme reactions, fuel cells, chemical sensors and electrochemical devices.

Contact details

Members of the Theoretical and Computational Chemistry group in Crete (TCCC):

• Prof. Stavros C. Farantos, Theoretical and Computational Chemistry, farantos (at) iesl.forth.gr
• Mr. Manos Giatromanolakis, IT Manager and Systems Analyst, gmanos (at) iesl.forth.gr
• Prof. Vangelis Daskalakis, Computational Biochemistry, chem487 (at) edu.uoc.gr
• Dr. Osvaldo Gervasi, Computer scientist, administrator of Compchem Virtual Organization,  osvaldo (at) unipg.it
• Dr. Massimiliano Porrini, Theoretical and Computational Chemistry, maxp (at) iesl.forth.gr
• Dr. Jaime Suarez, Theoretical and Computational Chemistry, jaime.suarez (at) iesl.forth.gr

References

1. V. Daskalakis, M. Giatromanolakis, M. Porrini, S. C. Farantos, and O. Gervasi, Computer Physics, Chapter: Grid computing multiple shooting algorithms for extended phase space sampling and long time propagation in Molecular Dynamics, Nova Science Publishing Co., 2011.
2. M. Porrini, V. Daskalakis, and S. C. Farantos, Thermodynamic Perturbation Calculations on Cytochrome c Oxidases interacting with small ligands, Phys. Chem. Chem. Phys., submitted, 2011.
3. Massimiliano Porrini, Vangelis Daskalakis, S. C. Farantos, and Constantinos Varotsis, Heme Cavity Dynamics of Photodissociated CO from ba3-Cytochrome c Oxidase: the Role of Ring-D Propionate, J. Phys. Chem. B, 113(35):12129-12135, 2009.

When using machine learning to learn how to play a game, vast amounts of experiments may be required to adequately explore the space of alternative tactics and strategies. To cover this need, grid was a technology which was exploited by the researchers.

The scientists have identified a curious phenomenon in game playing, which they term “the pendulum effect”. Therein, they use a tutor to provide playing advice to one player (A) of a zero-sum two-player game. It seems that the player (B) who does not receive tutoring is also able to enormously benefit just by being forced to play against a better player (A, as advised by the tutor). This is reinforced when the tutor abstains; player B seems to be able to win more often as compared to when the tutor is present directing A to more wins.

By using the HellasGrid and EGI infrastructure, the scientists conducted a range of experiments across hundreds of game configurations and across several learning schemes; some of them employed game tree look-ahead search which is pretty expensive (but not yet optimized for grid computing). These experiments may have consumed several tens of thousands of CPU hours on the grid; such availability of resources simply does not exist at stand-alone venues.

Future plans include the continuation of this line of research to verify this type of learning behavior, across more configurations and actions to verify it across other games as well. The scientists are also currently looking into workflows (hopefully, grid-aware workflow systems) to better organize data collection and analysis.

Contacts

• Dimitris Kalles, Hellenic Open University, kalles (at) eap.gr
• Panagiotis Kanellopoulos, Computer Technology Institute and Press “Diophantus”, kanellop (at) ceid.upatras.gr

Η υποδομή Tier-0 αποτελείται από τις υπερυπολογιστικές συστοιχίες:

• Jugene (IBM Blue Gene/P) στο Jülich της Γερμανίας και
• Curie (Bullx x86_64 cluster) στο Κέντρο ερευνών CEA στη Γαλλία.

Η πρόσκληση τύπου C αφορά στην ανάπτυξη κώδικα με την υποστήριξη από ειδικούς του PRACE και μέσω αυτής προσφέρονται εώς και 250.000 core hours στο Jugene και έως 200.000 core hours στο Curie ανά project.

Η συγκεκριμένη πρόσκληση αποσκοπεί στη δοκιμή και ανάπτυξη κώδικα για τη προετοιμασία εφαρμογών και όχι στην εκτέλεση εφαρμογών σε επίπεδο παραγωγής (production runs). Για την αξιολόγηση των προτάσεων είναι απαραίτητη αναλυτική περιγραφή των σημείων συμφόρησης (bottlenecks) του κάθε κώδικα καθώς επίσης και το προτεινόμενο πλάνο εργασίας.

Εφόσον ενδιαφέρεστε μπορείτε να επικοινωνήσετε με το KEEY Α.Π.Θ. για περισσότερες πληροφορίες.

In order to get access to HellasGrid infrastructure, you have to:

1. Obtain a Digital Certificate from the HellasGrid Certification Authority.
2. Get an account at a HellasGrid User Interface.
3. Join a Virtual Organization.
4. Import your Digital Certificate to the User Interface you obtained a personal account.

Obtain a Digital Certificate from the HellasGrid Certification Authority

In order to acquire a Digital Certificate you have to visit the following web page: https://access.hellasgrid.gr/. By clicking at the first choice, you can request a personal Digital Certificate. You have first to complete a form with your personal information (first name, last name, organization, department, etc). Once you have completed this form an informative e-mail will be sent at your personal e-mail account informing you that your personal information has been registered at HellasGrid database and request to confirm the reception of the e-mail. In case you do not confirm the e-mail reception in seven days, your registration will be removed from the HellasGrid data base.

Once you confirmed the reception of the e-mail you can proceed with the certification request procedure. Initially you will be asked to install at your web browser the certificate of the HellasGrid Certification Authority. For the Greek users the responsible authority is the HellasGrid Certification Authority (HellasGrid-CA) operated by the Department of Physics at the Aristotle University of Thessaloniki. Consequently you must generate your private key and certificate signing request at the web browser. The private key and certificate signing request will be automatically sent at HellasGrid-CA.

Once your certificate signing request has been sent at the HellasGrid-CA an informative email will be sent to you. With this email you will be requested to visit in person your appropriate Registration Authority and present the following documents:

1. Your identification card or your passport.
2. One document which will confirm your affiliation with your organization.
3. A printing of the received e-mail.

A list of the existing Registration Authorities for the Greek Users can be found at the following site. In the case you can not be served by an existing Registration Authority you must contact the Catch-all Registration Authority, operated by GridAUTH (mailto:hg-catch-all (at) grid.auth.gr).

You can regularly check the status of your certificate signing request. Once the status of your request changes to “signed” you have to install the certificate at your web browser (the same used for the procedure obtaining the certificate) and accept the terms of its use.

Get an account at a HellasGrid User Interface

A User Interface (UI) is nothing more and nothing less than a Linux box having installed all the required client software, APIs and tools for developing and running applications in the Grid. In practice everyone can install and setup a UI with the required EGEE tools following the instructions in the GLITE-3 Installation Guide.

There is also a chance that your institute may already have setup a UI machine so you can ask from your local administrator to create an account for you there.
Till now, six User Interfaces has been installed to serve the HellasGrid users:

• three in Athens (ui01.isabella.grnet.gr, ui02.isabella.grnet.gr, ui01.marie.hellasgrid.gr),
• one in Thessaloniki (ui01.afroditi.hellasgrid.gr),
• one in Heraklion (ui01.ariagni.hellasgrid.gr) and
• one in Patras (ui01.kallisto.hellasgrid.gr).

You can request access to the appropiate UI according to the location of your organization by following the second choice at the web page https://access.hellasgrid.gr. If you do not have access to a User Interface or you cannot (or do not want to) install your own UI, you may request via the second option of the web page https://access.hellasgrid.gr/ for an account at the Isabella catch-all UI hosted in the GRNET site, which is located at Athens; provided of course that you have already obtained a digital certificate issued by the HellasGrid Certification Authority. In order to connect at the User Interface at which you have an account you must use an ssh client program, for example putty, a free program which can be downloaded from the link http://www.chiark.greenend.org.uk/~sgtatham/putty/download.html.

Join a Virtual Organization

To be authorized to use the Grid and do useful work with it you have to belong to a Virtual Organization (VO); A Virtual Organization (VO) is a group of grid users with similar interests and requirements who are able to work collaboratively with other members of the group and/or share resources (data, software, expertise, CPU, storage space, etc.) regardless of geographical location. A list of existing EGEE VOs is available here.

In order to make life easier for the South Eastern Europe users (including the HellasGrid users), speed up and simplify the process of new application induction, EGEE-SEE has established its own VO called SEE-VO. This VO will be the most adequate for SEE users that do not fit in any of the existing EGEE VOs or are not able to create their own EGEE-wide VO. To join the SEE-VO as a HellasGrid user you have to request it via the third option of the site https://access.hellasgrid.gr. Please note that this page has to be visited using the browser on which you have already loaded your digital certificate otherwise the process cannot be completed.

Import your Digital Certificate to your User Interface

Till now, you have a Digital Certificate installed at your web browser. In order to use the HellasGrid infrastructure, for example to submit a job for execution, check the status of the job; get the output of the job, you have to install also your certificate to the User Interface you have an account.

First you have to export your Digital Certificate from your web browser at which it is installed. To do this at Internet Explorer you must follow the path: Tools->Internet Options->Content->Certificates->Personal->Export while to do this at Mozilla Firefox you must follow the path: Edit->Preferences->Advanced->Encryption->View Certificates->Your Certificates->Backup or Tools->Options->Advanced->Encryption->View Certificates->Your Certificates->Backup. In both cases your personal certificate must be saved with .p12 extension (PKCS#12 format). So, by following the instructions of the above links you have succesfully backed up your security certificate and private key at your machine. Now you have to copy your Digital Certificate from your machine to your home directory at the User Inteface you gained an account. Then you must create your Digital Certificate and private key in .pem format. In order to do this you must execute the following two openssl commands:

openssl pkcs12 -nocerts \

         -in mycertificate.p12 \

         -out ~user/.globus/userkey.pem

openssl pkcs12 -clcerts -nokeys \

        -in mycertificate.p12 \

        -out ~user/.globus/usercert.pem

The first openssl command gets as input your certificate in .p12 format (mycertificate.p12) and creates your private key in .pem format (userkey.perm).The second openssl command gets as input your certificate in .p12 format (mycertificate.p12) and creates your certificate in .pem format (usercert.pem). We must mention that the ~ user should be replaced by the path to your home area. Both your private key and certificate are stored in the .globus directory.

Finally you must give the appropiate read privileges at your private key and certificate.

chmod 444 ~/.globus/usercert.pem

chmod 400 ~/.globus/userkey.pem

The computational models used for these simulations are the regional climate model RegCM3 and the air quality model CAMx, which have been off line coupled in this case (Figure 1). The control simulation for the decade 1991-2000 was performed twice, once externally forced by the ERA40 reanalysis and once using the global circulation model ECHAM5, in order to investigate the importance of external meteorological forcing on air quality (Katragkou et al., 2010). The RegCM3 model was forced by ECHAM5 under the A1B emission scenario for two future time slices, namely 2041-2050 and 2091-2100. These simulations served as a theoretical experiment of evaluating the impact of climate change on air pollution (Katragkou et al., 2011).

For each decadal simulation the computation consumed approximately 1000 CPU hours. CAMx simulations were performed on SMP machines as the model has been intrinsically parallelized with the OpenMP library. Using this feature of the CAMx model a significant reduction on the overall computation time was feasible.

In terms of storage, the resources required for archiving the CAMx output files are estimated to slightly more than 5TB.

Figure 1: A schematic illustrating an outline of the modelling system RegCM3/CAMx applied in this study (from Zanis et al., 2011)

Surface ozone simulated by RegCM3/CAMx was evaluated against ground based measurements from the European database EMEP (Zanis et al., 2011). The air quality simulations available at AUTH for the three time slices (1991-2000, 2041-2050 and 2091-2100) over Europe with a resolution of 50 Km have been provided as air quality boundaries for higher resolution air quality simulations over sub-European grids (Huszar et al., 2011).

The results suggest that changes imposed by climate change until the 2040s in surface ozone concentration during summer will be below 1 ppbv (parts per billion by volume) . By the 2090s, however, changes are foreseen to be more significant especially over south-west Europe, where the median of near surface ozone has been found to increase by 6.2 ppbv.

Figure 2: Average summer surface ozone for the control simulation 1991-2000 (left). Differences in simulated average summer ozone between 2091-2100 and control simulation (right). The grey color corresponds to non-statistical significant differences (from Katragkou et al., 2011)

The median of summer near surface temperature for Europe at the end of the 21st century was calculated at 2.7K higher than the end of the 20th century with more intense temperature increase simulated for southern Europe. A prominent outcome was the decrease of cloudiness mostly over western Europe at the end of the 21st century associated with an anticyclonic anomaly which favours more stagnant conditions and weakening of the westerly winds (Katragkou et al., 2011).

Figure 3: Mean differences between second future decade (2091-2100) and the present decade (1991-2000) for summer in the fields of surface temperature (left) and geopotential height at 500 hPa (right).The red contours correspond to geopotential height at 500 hPa during the control decade (from Katragkou et al., 2011).

This work has been accomplished in the framework of the FP6 European Project CECILIA (Central and Eastern Europe Climate Change Impact and Vulnerability Assessment, Contract Nr 037005). The results were produced on the EGI and HellasGrid infrastructure with the support of the Scientific Computing Center at the Aristotle University of Thessaloniki (AUTH). The results of this work have been published in peer-review journals (see references), presented in several national and international conferences and awarded by the Hellenic Meteorological Society (2008), the European Association for the Science of Air Pollution (2009) and the Research Committee of the Aristotle University of Thessaloniki (2010).

Contact details:

• Dimitris Melas (PI), Associate Professor, AUTH, melas (at) auth.gr
• Prodromos Zanis, Assistant Professor, AUTH, zanis (at) geo.auth.gr
• Eleni Katragkou, Lecturer, AUTH, katragou (at) auth.gr
• Scientific Computing Center, AUTH, contact (at) grid.auth.gr

References:

1. Huszar P., K. Juda-Rezler, T. Halenka, H. Chervenkov, D. Syrakov, B. C. Krueger, P. Zanis, D. Melas, E. Katragkou, M. Reizer, W. Trapp, M. Belda, Potential climate change impacts on ozone and PM levels over Central and Eastern Europe from high resolution simulations, Climate Research (in press), 2011
2. Katragkou Ε., P. Zanis, I. Tegoulias, D. Melas, I. Kioutsioukis, B. C. Krüger, P. Huszar, T. Halenka, S. Rauscher, Decadal regional air quality simulations over Europe in present climate: near surface ozone sensitivity to external meteorological forcing, Atmospheric Chemistry and Physics, 10, 11805-11821, 2010
3. Κatragkou E., P. Zanis, I. Kioutsioukis, I. Tegoulias, D. Melas, B.C. Krüger, E. Coppola, Future climate change impacts on summer surface ozone from regional climate-air quality simulations over Europe, J Geophys Res (in press), 2011
4. Zanis P., E. Katragkou, I. Tegoulias, A. Poupkou, D. Melas, Evaluation of near surface ozone in air quality simulations forced by a regional climate model over Europe for the period 1991-2000, Atmospheric Environment, 45, 6489-6500, 2011

Περισσότερες πληροφορίες για τη διαδικασία αίτησης συμμετοχής και το πρόγραμμα μπορούν να βρουν οι ενδιαφερόμενοι στην επίσημη σελίδα του σχολείου.

Περισσότερα στο http://repository.egi.eu

Στόχος του προγράμματος Guest Student Programme είναι να έρθουν σε επαφή με το χώρο του scientific computing νέοι επιστήμονες από τους χώρους της μηχανικής, των θετικών επιστημών και της πληροφορικής και να εμβαθύνουν τις γνώσεις τους σε θέματα παράλληλου προγραμματισμού, παράλληλων αλγορίθμων, διαχείρισης και οπτικοποίησης δεδομένων και υπερυπολογιστικών υποδομών γενικότερα, ανάλογα με τα ενδιαφέροντά τους.

Η επίσημη γλώσσα του προγράμματος είναι τα Αγγλικά. Η προθεσμία αίτησης συμμετοχής είναι 13 Μαϊου 2011 και οι ενδιαφερόμενοι μπορούν να βρουν περισσότερες πληροφορίες για το πρόγραμμα και τη διαδικασία αίτησης συμμετοχής στην επίσημη σελίδα του προγράμματος.