Exploring the dataflow supercomputing paradigm : example Algorithms for Selected Applications Intro Preface Contents Contributors Part I Theoretical Issues 1 Method of Big Graph Partitioning Using a Skeleton Graph 1.1 Introduction 1.1.1 Proposed Method of Graph Partitioning 1.1.2 Contributions 1.1.3 Overview 1.2 Distributed Triple Store Setup 1.2.1 Architecture 1.2.2 Distributed Query Execution System 1.3 Formalization and Statistics 1.3.1 Formalization 1.3.2 Computing Statistics 1.4 Graph Partitioning Method 1.4.1 Semantic Distribution 1.4.2 Computing Skeleton Graph 1.4.3 Clustering Skeleton Graph 1.4.4 Triple Pattern Localization 1.4.5 Related Work 1.5 Empirical Evaluation 1.5.1 Benchmark Environment 1.5.2 Benchmark Results on Different Distribution Algorithms 1.6 Conclusion References 2 On Cloud Supported Web Based Integrated Development Environment for Programming DataFlow Architectures 2.1 Introduction 2.2 The Control Flow Hardware 2.3 The DataFlow Hardware 2.4 The Maxeler Framework 2.5 The MaxIDE Framework 2.6 The WebIDE Framework 2.7 Conclusion References Part II Applications in Mathematics 3 Minimization and Maximization of Functions: Golden Section Search in One Dimension 3.1 Introduction 3.2 Existing Solutions 3.3 Essence of the DataFlow Paradigm 3.4 Minimization or Maximization of Functions 3.4.1 Unimodality 3.5 Golden Section Search 3.5.1 Derivation of the Method 3.5.2 The control flow Implementation 3.5.3 The DataFlow Implementation 3.6 Performance Evaluation 3.6.1 MAX4 Card Usage Evaluation 3.6.2 Execution Time 3.6.3 Test Examples 3.6.4 Test Results and Comparison with control flow paradigm 3.6.5 Cluster Testing 3.7 Conclusion References 4 Matrix Based Algorithms for DataFlow Computer Architecture: An Overview and Comparison 4.1 Introduction.