ThaiGrid and E-science in Thailand Putchong Uthayopas Vara Varavithya Director High Performance Computing and Networking Center Kasetsart University, Bangkok, Thailand [email protected] Department of Electrical Engineering Faculty of Engineering KMITNB, Bangkok, Thailand [email protected] ThaiGrid A partnership project to explore grid computing technology and application in Thailand. Project started since December 2000 Link: http://www.thaigrid.net Currently funded by National Research Council of Thailand (NRCT) 1.0 Million
Commission on Higher Education, Ministry of Education (2.6 Million) Infrastructure funded by KU, KMITNB, SUT NTL NECTEC Contact Putchong Uthayopas, KU ([email protected]) Vara Varavidthaya, KMITNB ([email protected]) 2 Members 7 universities
Kasetsart University King Mongkuts Institute of Technology North Bangkok Suranaree University of Technology Asian Institute of Technology Chulalongkorn University Walailak University Chiangmai University 1 Government Agency National Electronics and Computing Technology 3 Goal Create a grid computing infrastructure for Thai researchers Stimulate the deployment of Grid Computing Technology Build a collaborative Research Network among Thai researchers
Act as a focal point for international grid collaboration 4 Organization Chair Steering Commitee Grid Infrastructure and Middleware Group Simulation Group Working Group Computational Chemistry Group CFD Group
FEM Group Remote Sensing Group Evolutionary Comp. Group 5 Activities Building ThaiGrid Testbed Research Tools Applications International Collaboration ApGrid
PRAGMA APAN 6 OBSERVER ThaiGrid System MAEKA WARINE HPCNC CU AMATA KU KMITNB PALM NECTEC GASS WU
ThaiGrid CMU AIT OPTIMA SUT CAMETA 7 Current Internet in Thailand http://www.ntl.nectec.or.th/internet/index.html 8 Bandwidth 2003/10 2002/12 Total
International Bandwidth (Mbps) To Thailand Total International Bandwidth (Mbps) From Thailand Thailand's Internal Bandwidth 12000 B a n d w id t h ( M b p s ) Year 2002/02 2001/04 2000/06 1999/08 1998/10
1GB RAM 80 GB HDD Gigabit Ethernet KMITNB OBSERVER 1 nodes Athlon 1800+ 512 MB RAM, 80 GB HDD SUT CAMETA 16 nodes Athlon XP1800+ Fast Ethernet Total of 148 processors on ThaiGrid 11 ThaiGrid Software Architecture Grid Applications Grid Tools Grid Resources
Grid RPC Manager (SCEGrid) (ninf) Grid Middleware (Globus 2.4) LRM LRM LRM LRM LRM NECTEC Computing System KU Computing System KMITNB Computing System
SUT Computing System AIT Computing System LRM=Local Resources Manager 12 Software Local Resources Management Condor SQMS (KU) SGE (Planned) Middleware Globus 2.4
Grid Level Resource Management SCEGrid Scheduler (KU) Data Grid Gfarm Data Grid (AIST) Grid Programming Environment Ninf GridRPC (AIST) MPICH-G2 Tools SCMSweb Monitoring (KU) 13 Tools Development OpenSCE: Cluster software Tools and
Middleware (KU) MPview MPI program visualization MPITH Quick and simple MPI runtime for cluster and grid SQMS Batch scheduler for cluster SCMS/ SCMSWEB cluster management tool ThaiGrid Portal (KMITNB) HypersimGrid Simulator for Grid design (KU) 14 SCMS Web Monitoring 15 ThaiGrid Portal Data Manage. Web-base Compilers. Jobs Submittion.
Jobs Manage. Resources Monitoring. Automatic and Manual generate RSL. User Management. Portal systems configuration. 16 ThaiGrid Portal Portal are centralize of grid computing. Middle tier between grid servers and grid users. Developed on Web technology. Allocate the appropriate resources. Use XML for standard document. Use web account only, Portal CA. 17 ThaiGrid Portal Jobs function Scheduler Job. Generate RSL files. Supports serial and parallel jobs.
18 Portal User function Registration. Activate/deactivate account. Edits user information. 19 Application Computational Fluid Dynamics Simulation Scheduling PGA Pack Computational Chemistry GAMESS(General Atomic and Molecular Electronic Structure System) FEM in High Voltage Insulator Evolutionary Computing
20 Clean Room Project Member: KU, SUT Goal: study clean room using CFD Three-Dimensional Turbulence Problem Heat & Mass Transfer Using: Finite volume, Multigrid, Parallel computing Solution: Grid is used to Provided uniform security mechanism across the cluster computing environment Provide mechanism for large scale data access Tools
Globus , MPICH Grid RPC (ninf, netsolve) Gfarm data grid 21 Software Structure Parallel CFD Solver Front End Sequential Solver Visualization Network Parallel CFD Solver Front End Sequential Solver Visualization 22
Operation User Input Problem gridview ACI SQMS Scview Parallel Solver 23 Simulation Many simulation and optimization problem can utilized grid and cluster well Parametric applications is perfect for grid Simulation job on the ThaiGrid Genetic algorithm for optimization problem using PGApack
Grid simulation (HyperGridSim) 24 Solution Running on cluster using batch scheduler Deploy over Grid Using SCE/Grid scheduler Tools Globus SCE/Grid SQMS, SGE, OPENPBS 25 Computational Chemistry Laboratory for Computational and Applied Chemistry (LCAC), KU. Research
Zeolite Chemistry & Catalysis Surface Structure & Reactivity of Advanced Materials calculate molecular structures and properties of HIV-1 inhibitors in the class of non-nucleoside derivatives and to create quantitative structureactivity relationships (QSAR) model, based on both classical and 3Dimensional QSAR. 26 Solution Running GAMESS on cluster (currently) Deploy GAMESS over Grid Using SCE/Grid scheduler Tools
Globus SQMS/Grid SQMS, SGE, OPENPBS 27 Remote Sensing Star Project (KU/AIT) Deploy cluster and grid for remote sensing application Analysis of the impact of irrigation system using image processing and genetics algorithm Approach Using gridrpc for parallelization Using batch scheduler for GA simulation 28
Parallel Electric field Calculation : High Performance Library Integrated Approach Analyze electrical stress on Three Phases Power Cable 948,018 nodes and 1,887,408 elements Parallel Electric field Calculation : High Performance Library Integrated Approach Analyze electrical stress on High Voltage Insulator 680,583 nodes and 1,357,963 elements Evolutionary Computation: Theories and Applications in Engineering, Biology, and Medicine
Investigators: Nachol Chaiyaratana and Vara Varavithya Evolutionary computation concerns theories and applications of biologically inspired algorithms. Similar to biological systems, the solution s generated by these algorithms are allowed to emerge or change throug h the processes of evolution or adaptation as guided by external stimuli. Our research interests cover both theories and applications of various te chniques including genetic algorithms, genetic programming and ant colo ny system algorithms. Theory 1. Multi-Objective Co-Operative Co-Evolutionary Genetic Algorithm 2. Diversity Control in a Multi-Objective Genetic Algorithm Application 1. Wireless LAN Access Point Placement using a Multi-Objective Genetic Algorithm 2. DNA Fragment Assembly using an Ant Colony System Algorithm 3. Thalassemic Patient Classification using a Neural Network and Genetic Programming 31 Multi-Objective Co-Operative Co-Evolutionary Genetic Algorithm Investigators: Nuttavut Keerativuttitumrong, Nachol Chaiyaratana and Vara Varavithya 4.5
0.4 0.5 0.6 objective 1 0.7 0.8 0.9 1 Integration between two types of genetic algorithm: a multi-objective genetic algorithm (MOGA) and a co-operative co-evolutionary genetic algorithm (CCGA) Improve the performance of the MOGA by adding the co-operative co-evolutionary effect to the search mechanisms employed by the MOGA In overall the MOCCGA is superior to the MOGA in terms of the variety in solutions generated and the closeness of solutions to the true Paretooptimal solutions With the use of an 8-node cluster, the speed-up of 2.64 to 4.8 can be achieved for the test
problems 32 Diversity Control in a Multi-Objective Genetic Algorithm Investigators: Nuntapon Sangkawelert and Nachol Chaiyaratana c = 0 , = 0.2 c = 0.5, = 0.2 c = 0 , = 1 c = 0.5, = 1 c = 1 , = 1 2.5 2 Objective 2 1.5
0.6 0.7 0.8 The diversity control operator used is based on the one developed for a diversity control oriented genetic algorithm (DCGA). The performance comparison between multiobjective genetic algorithms with and without diversity control is explored where different benchmark problems with specific multi-objective characteristics are utilised. The results indicate that the use of diversity control with specific parameter settings promotes the emergence of multi-objective solutions that are close to the true Pareto optimal solutions while maintaining a uniform distribution of the solutions along the Pareto front. 33 Wireless LAN Access Point Placement using a Multi-Objective Genetic Algorithm Investigators: Kotchakorn Maksuriwong, Vara Varavithya
and Nachol Chaiyaratana The aim is to maximise signal coverage over an interested area. The decision variables are derived from the locations of the access points. The objectives consist of the number of access points and the average SNR over the whole area. The MOGA is capable of generating a placement result which is superior to that produced using standard placement techniques. Multiple optimal placement configurations for different numbers of access points can be obtained from a single run of the MOGA. 34 DNA Fragment Assembly using an
Ant Colony System Algorithm Investigators: Prakit Meksangsouy and Nachol Chaiyaratana The aim is to find the right order and orientation of each fragment in the fragment ordering sequence that leads to the formation of a consensus sequence. An asymmetric ordering representation is proposed where a path co-operatively generated by all ants in the colony represents the search solution. The optimality of the fragment layout is obtained from the sum of overlap scores calculated for each pair of consecutive fragments. The ant colony system algorithm outperforms the nearest neighbour heuristic algorithm when multiple-contig problems are considered.
35 Thalassemic Patient Classification using a Neural Network and Genetic Programming Investigators: Waranyu Wongseree and Nachol Chaiyaratana y n o n -te rm in a ln o d e
te rm in a ln o d e z 2 z 1 x 1 x
2 x 3 x 4 Using a genetic programming (GP) system called STROGANOFF and a multilayer perceptron in thalassemic patient classification The problem covers the test samples from normal subjects and that from different types of thalassemic patient and thalassemic trait. The characteristics of red blood cell, reticulocyte and blood platelet are used as input. The performance of the GP-generated classification trees is approximately equal to that of the multilayer perceptrons. The structure of the classification trees reveals that the characteristics of blood platelet have no effects on the classification performance. 36
Related Project Thai e-science project New project funded in 2003 (3 Million) Application oriented project Current members Computational Chemistry Unit Cell, Department of Chemistry, Chulalongkorn University Department of Computer Engineering, Chulalongkorn University HPCNC, Kasetsart University Contact:http://www.thai-escience.net/ Dr. Prabhas Chongstitvatana (Associate Professor, Intelligent System Lab, Department of Computer Engineering, Chulalongko rn University)[email protected]
37 Conclusion Grid is a promising technology but Lack manpower and expertise Difficult to setup , steep learning curve The awareness of Grid and E-science in Thailand is still at the very beginning There is a need to Build larger community, focus more on education and out-reach program Build strong testbed first Find killer applications 38 Future Plan
Building easy to use and stable environment Attract more user and more applications Bioinformatics Nanotechnology Find new area to deploy grid technology Education, technology transfer 39 The end
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