DataFlow Supercomputing Essentials : Algorithms, Applications and Implementations.

Intro -- Preface -- Dataflow Programming -- Dataflow Compilation -- Programming Maxeler -- Application Issues -- Conclusion -- Contents -- About the Authors -- Part I Algorithms -- 1 Implementing Neural Networks by Using the DataFlow Paradigm -- 1.1 Introduction -- 1.2 Existing Solutions -- 1.3 Essence of the dataflow Paradigm -- 1.4 Artificial Neural Networks -- 1.5 Single Layer Perceptron -- 1.5.1 Algorithm -- 1.5.2 The dataflow Implementation -- 1.6 Multilayer Perceptron Algorithm -- 1.6.1 Backpropagation Algorithm -- 1.6.1.1 Feed-Forward Phase -- 1.6.1.2 Feedback Phase -- 1.6.2 The dataflow Implementation -- 1.7 Performance Evaluation -- 1.7.1 Single Layer Perceptron -- 1.7.2 Multilayer Perceptron -- 1.8 Conclusion -- References -- Part II Applications -- 2 Solving the Poisson Equation by Using DataFlow Technology -- 2.1 Introduction -- 2.2 Mathematical Background -- 2.2.1 Boundary Conditions -- 2.3 Overview of Existing Solutions -- 2.3.1 Fourier Techniques -- 2.3.2 Iterative Techniques -- 2.3.2.1 Jacobi Method -- 2.3.2.2 Gauss-Seidel Method -- 2.3.2.3 Successive Over-relaxation Method -- 2.3.3 Multigrid Techniques -- 2.4 Overview of the Goal Solution -- 2.4.1 Maxeler dataflow Computing Technology Concept -- 2.4.2 Conditions of Applicability -- 2.4.3 Implementation Details -- 2.4.3.1 Kernel -- 2.4.3.2 PoissonSolver -- 2.4.3.3 Manager -- 2.4.4 User Guide -- 2.5 Measurements, Results, and Performances -- 2.6 Practical Applicability -- 2.6.1 Astrophysics -- 2.6.2 Electrostatics -- 2.6.3 Biology and Related Disciplines -- 2.6.4 Team Behavior Analysis -- 2.6.5 Other Applications -- 2.7 Conclusion -- References -- 3 Binary Search in the DataFlow Paradigm -- 3.1 Introduction -- 3.2 System Realization Requirements -- 3.2.1 The Problem -- 3.2.2 About Maxeler Technology -- 3.2.3 Conditions and Assumptions -- 3.2.4 Existing Solutions.

3.3 Algorithm Description -- 3.3.1 Data Preparation -- 3.3.2 Binary Search Algorithm -- 3.4 DataFlow Algorithm Realization -- 3.4.1 Kernel Code -- 3.4.2 C Code -- 3.4.3 Manager Code -- 3.5 Performance Analysis -- 3.5.1 Test Results -- 3.5.2 Longer Input Sequence Analysis -- 3.5.3 Bottleneck Analysis -- 3.6 Conclusion -- References -- Part III Implementations -- 4 Introductory Overview on Implementation Tools -- 4.1 Introduction to dataflow Programming -- 4.2 DataFlow Engines (DFEs) and MaxJ Programming -- 4.3 Porting a Program to dataflow System -- 4.3.1 Program Analysis -- 4.3.2 Evaluating Different Architectural Options -- 4.3.3 Implementing the Kernels -- 4.3.4 Integration of the Accelerated Kernels Back into the Original Program -- 4.3.5 Debugging and Optimizing the Implementation to Get Maximal Performance -- 4.4 Interfaces -- 4.4.1 SAPI Single-DFE Single-Action Interface -- 4.4.2 MAPI Multiple Action Interface -- 4.4.3 DAPI DFE Creation API -- 4.5 Tools Overview -- 4.5.1 Maxtop -- 4.5.2 MaxIDE -- 4.5.3 MaxPower -- 4.5.4 Monitoring Dashboard -- 4.5.5 Maxdebug -- 4.5.6 Sliccompile -- 4.5.7 MaxSkins (DFEs Speak Your Language) -- 4.5.8 MaxOrchestrator -- References -- 5 DataFlow Systems: From Their Origins to Future Applications in Data Analytics, Deep Learning, and the Internet of Things -- 5.1 Introduction -- 5.2 High-Performance Computing Technology -- 5.2.1 State of the Control-Flow Technology -- 5.2.2 Special-Purpose Systems -- 5.2.3 DataFlow Systems -- 5.2.4 DataFlow Languages -- 5.2.5 Systolic Architecture -- 5.2.6 Multiscale dataflow Computing Approach -- 5.3 Ideas for New Applications of dataflow Systems -- 5.3.1 Software Distributed dataflow Systems for Big Data Analytics -- 5.3.2 Integration of MDC System with Apache Spark -- 5.3.3 Deep Learning Applications -- 5.3.4 The Internet of Things Applications -- 5.4 Conclusion -- References.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.