Exploring the DataFlow Supercomputing Paradigm: Example Algorithms for Selected Applications
Veljko / Kotlar Milutinovic
Résumé
The mapping of additional algorithms onto the DataFlow architecture is also covered in the following Springer titles from the same team: DataFlow Supercomputing Essentials: Research, Development and Education , DataFlow Supercomputing Essentials: Algorithms, Applications and Implementations , and Guide to DataFlow Supercomputing .
Topics and Features: introduces a novel method of graph partitioning for large graphs involving the construction of a skeleton graph; describes a cloud-supported web-based integrated development environment that can develop and run programs without DataFlow hardware owned by the user; showcases a new approach for the calculation of the extrema of functions in one dimension, by implementing the Golden Section Search algorithm; reviews algorithms for a DataFlow architecture that uses matrices and vectors as the underlying data structure; presents an algorithm for spherical code design, based on the variable repulsion force method; discusses the implementation of a face recognition application, using the DataFlow paradigm; proposes a method for region of interest-based image segmentation of mammogram images on high-performance reconfigurable DataFlow computers; surveys a diverse range of DataFlow applications in physics simulations, and investigates a DataFlow implementation of a Bitcoin mining algorithm.
This unique volume will prove a valuable reference for researchers and programmers of DataFlow computing, and supercomputing in general. Graduate and advanced undergraduate students will also find that the book serves as an ideal supplementary text for courses on Data Mining, Microprocessor Systems, and VLSI Systems.
Part I: Theoretical Issues
A Method for Big-Graph Partitioning Using a Skeleton Graph
Iztok Savnik and Kiyoshi Nitta
On Cloud-Supported Web-Based Integrated Development Environments for Programming DataFlow Architectures
Nenad Korolija and Ales Zamuda
Part II: Applications in Mathematics
Minimization and Maximization of Functions: Golden Section Search in One Dimension
Dragana Pejic and Milos Arsic
Matrix-Based Algorithms for DataFlow Computer Architecture: An Overview and Comparison
Jurij Mihelic and Uros Cibej
Application of Maxeler DataFlow Supercomputing to Spherical Code Design
Ivan Stanojevic, Mladen Kovacevic, and Vojin Senk
Part III: Applications in Image Understanding, Biomedicine, Physics Simulation, and Business
Face Recognition Using Maxeler DataFlow
Tijana Sustersic, Aleksandra Vulovic, Nemanja Trifunovic, Ivan Milankovic, and Nenad Filipovic
Biomedical Image Processing Using Maxeler DataFlow Engines
Aleksandar S. Peulic, Ivan L. Milankovic, Nikola V. Mijailovic, and Nenad D. Filipovic
An Overview of Selected DataFlow Applications in Physics Simulations
Nenad Korolija and Roman Trobec
Bitcoin Mining Using Maxeler DataFlow Computers
Rok Meden and Anton Kos
Mr. Milos Kotlar is a Software Engineer at the Swiss-Swedish company ABB (ASEA Brown Boveri) of Zurich, Switzerland and a Ph.D. student at the School of Electrical Engineering at the University of Belgrade, Serbia. He serves as a TA for DataFlow supercomputing courses and as an RA for DataFlow supercomputing research in the domain of tensor calculus.
Caractéristiques techniques
| PAPIER | |
| Éditeur(s) | Springer |
| Auteur(s) | Veljko / Kotlar Milutinovic |
| Parution | 12/06/2019 |
| Nb. de pages | 315 |
| EAN13 | 9783030138028 |
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