Prodan, Radu

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Radu Prodan Thomas FahringerGrid ComputingExperiment Management, Tool Integration,and Scientific Workflows.:1 SpringerContents1 Introduction 	 	 11.1 Motivation 	 	 21.1.1 Performance Tuning 	 	 21.1.2 Parameter Studies 	 	 31.1.3 Optimisation 	 	 31.1.4 Scheduling 	 	 31.1.5 Parametrisation Language 	 	 41.1.6 Instrumentation 	 	 41.1.7 Portability 	 	 51.1.8 Tool Interoperability 	 	 51.1.9 Grid Services 	 	 51.1.10 Scientific Workflows 	 	 61.2 Goals 	 	 61.2.1 Experiment Specification Language 	 	 61.2.2 Experiment Management Tool 	 	 71.2.3 Optimisation 	 	 81.2.4 Scientific Workflows 	 	 81.2.5 Service-Oriented Grid Architecture 	 	 91.2.6 Grid Services 	 	 101.3 Outline 	  102 Model 	  132.1 Introduction 	  132.2 Distributed Technology History 	  142.3 Web Services 	  152.3.1 Web Services Stack 	  162.3.2 Web Services Runtime Environment 	  182.4 Grid Security Infrastructure 	  192.5 Globus Toolkit 	  202.6 Grid Architectural Model 	  222.6.1 Machine Layer 	  222.6.2 Grid Services Layer 	  282.6.3 Application Layer 	  302.7 Summary 	  35XII	 Contents3 The ZEN Experiment Specification Language 	  373.1 Functionality and Use Cases 	  373.1.1 Shared Memory Application Scalability 	  383.1.2 ZEN Transformation System 	  393.1.3 Shared Memory Loop Scheduling 	  403.1.4 Distributed Processor Arrays 	  413.1.5 Distributed Memory Arrays 	  413.1.6 Work Distribution 	  433.1.7 Parameter Studies 	  433.2 Formal Language Specification 	  443.2.1 ZEN Set 	  443.2.2 ZEN Directives 	  493.2.3 ZEN Substitute Directive 	  503.2.4 Local Substitute Directive 	  513.2.5 Homonym ZEN Variables 	  513.2.6 ZEN Assignment Directive 	  533.2.7 Multi-dimensional Value Set 	 543.2.8 ZEN Constraint Directive 	 553.2.9 ZEN Performance Directive	 593.2.10 Parameter Study Experiment 	 623.2.11 Experiment Generation Algorithm	 623.2.12 Online Monitoring and Analysis 	 653.3 Summary 	  684 ZENTURIO Experiment Management Tool 	  694.1 User Portal Functionality	 694.1.1 ZEN Editor 	  704.1.2 Experiment Preparation	 714.1.3 Experiment Monitor 	 734.1.4 Application Data Visualiser	 734.2 Performance Studies 	 764.2.1 Ocean Simulation 	 764.2.2 Linearised Augmented Plane Wave 	 794.2.3 Three-Dimensional Particle-in-Cell 	 844.2.4 Benders Decomposition 	 864.2.5 Three-Dimensional FFT Benchmarks 	 894.3 Parameter Studies 	 944.3.1 Backward Pricing 	 944.4 Architecture 	 1054.4.1 Experiment Generator 	 1074.4.2 Experiment Executor 	 1084.4.3 Experiment State Transition Diagram	 1104.4.4 Experiment Data Repository	 1104.5 Summary 	  111Contents XIII5 Tool Integration 	 1135.1 Architecture 	 1145.2 Interoperable Tool Set 	 1165.2.1 Object Code Browser 	 1175.2.2 Function Profiler (Z_prof) 	 1175.2.3 Function Tracer (Z_trace) 	  1185.2.4 Function Coverager (Z_cov) 	 1195.2.5 Sequential Debugger (Z_debug) 	 1215.2.6 Memory Allocation Tool (Z_MAT) 	 1215.2.7 Resource Tracker (Z_RT2) 	  1225.2.8 Deadlock Detector (Z_deadlock) 	  1225.3 Tool Interoperability 	  1225.3.1 Classification 	  1225.3.2 Interaction with a Browser 	  1235.3.3 Performance Steering 	  1245.3.4 Just-in-Time Debugging 	  1265.3.5 Interaction with a Debugger 	  1275.4 The Monitoring Layer 	  1285.4.1 Dynamic Instrumentation 	  1285.4.2 The Process Manager 	  1305.4.3 Dynamic Instrumentation of MPI Applications	 1345.5 The Grid Services Layer 	  1365.5.1 Web Application and Services Platform (WASP) 	 1385.5.2 Service Repository 	  1395.5.3 Abstract Grid Service 	  1405.5.4 Factory 	  1425.5.5 Registry 	  1435.5.6 WSDL Compatibility 	  1445.5.7 Dynamic Instrumentor 	  1445.5.8 Aggregator 	  1455.6 Event Framework 	  1465.6.1 Representation 	  1465.6.2 Implementation 	  1495.6.3 Filters 	  1515.7 Firewall Management 	  1515.8 WASP Versus GT3 Technology Evaluation 	  1525.8.1 Stub Management 	  1535.8.2 Service Lifecycle 	  1545.8.3 UDDI-Based Service Repository 	  1555.8.4 Service Data 	  1555.8.5 Events 	  1555.8.6 Registry 	  1565.8.7 Security 	  159XIV Contents5.8.8 Grid Service Throughput 	  1605.8.9 Comparison 	  1635.9 Summary 	  1646 Optimisation Framework 	  1656.1 Workflow Scheduling 	  1676.1.1 Schedule Dependencies 	  1696.1.2 Objective Function 	 1706.2 Genetic Search Engine 	 1746.2.1 Initial Population 	 1756.2.2 Selection 	 1776.2.3 Crossover 	 1776.2.4 Mutation 	 1786.2.5 Elitist Model 	 1786.2.6 Fitness Scaling 	 1796.2.7 Convergence Criterion 	 1806.3 Genetic Workflow Scheduling 	 1806.3.1 WIEN2k 	 1806.4 Throughput Scheduling 	 1926.5 Performance Tuning of Parallel Applications 	 1946.5.1 Parallel Applications an the Grid 	 1956.6 Summary 	 2017 Scientific Grid Workflows 	 2037.1 Workflow Model 	 2047.1.1 Computational Activity 	 2057.1.2 Control Flow Dependencies 	 2067.1.3 Data Flow Dependencies 	 2077.1.4 Conditional Activity 	 2077.1.5 Parallel Loop Activity 	 2087.1.6 Sequential Loop Activity 	 2117.1.7 Workflow Activity 	 2137.2 Scheduler	 2147.2.1 Workflow Converter 	 2147.2.2 Scheduling Engine	 2207.2.3 Layered Partitioning	 2267.2,4 WIEN2k 	 22 7:i7.2.5 Invmod 	 2317.3 Enactment Engine	 57.3.1 Workflow Partitioning 	 2337.3.2 Control Flow Management 	  22,1;$7.3.3 Data Flow Management,	 2'127.3.4 Virtual Single Execution Environment 	 2437.3.5 Workflow Steering	  2117.3.6 Fault Tolerancc., 	 248Contents XV7.3.7 WIEN2k Execution Experiments 	  2537.3.8 Steering Experiments 	  2557.4 Overhead Analysis 	  2607.4.1 Experiments 	  2637.5 Summary 	  2698 Related Work 	  2718.1 Experiment Management 	  2718.2 Performance Study 	  2718.3 Parameter Study 	  2738.4 Optimisation and Scheduling 	  2738.5 Tool Integration 	  2748.5.1 Scientific Workflows 	  2769 Conclusions 	  2799.1 Contributions 	  2799.1.1 Experiment Specification 	  2799.1.2 Experiment Management 	  2809.1.3 Optimisation 	  2819.1.4 Tool Integration Design 	  2819.1.5 Web Services for the Grid 	  2839.1.6 Scientific Workflows 	  28310 Appendix 	  28510.1 Notations 	  28510.2 Code Regions 	  28810.3 Abbreviations 	  28910.4 Performance Metrics 	  292References 	  297Index 	  311

Grid Computing: Experiment Management, Tool Integration, and Scientific Workflows

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