Introduction to HPC with MPI for Data Science

This gentle introduction to High Performance Computing (HPC) for Data Science using the Message Passing Interface (MPI) standard has been designed as a first course for undergraduates on parallel programming on distributed memory models, and requires only basic programming notions. Divided into two...

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Détails bibliographiques
Cote:Libro Electrónico
Auteur principal: Nielsen, Frank (Auteur)
Collectivité auteur: SpringerLink (Online service)
Format: Électronique eBook
Langue:Inglés
Publié: Cham : Springer International Publishing : Imprint: Springer, 2016.
Édition:1st ed. 2016.
Collection:Undergraduate Topics in Computer Science,
Sujets:
Computer programming.
Programming Techniques.
Accès en ligne:Texto Completo
Table des matières:
  • Preface
  • Part 1: High Performance Computing (HPC) with the Message Passing Interface (MPI)
  • A Glance at High Performance Computing (HPC)
  • Introduction to MPI: The Message Passing Interface
  • Topology of Interconnection Networks
  • Parallel Sorting
  • Parallel Linear Algebra.-The MapReduce Paradigm
  • Part 11: High Performance Computing for Data Science
  • Partition-based Clustering with k means
  • Hierarchical Clustering
  • Supervised Learning: Practice and Theory of Classification with k NN rule
  • Fast Approximate Optimization to High Dimensions with Core-sets and Fast Dimension Reduction
  • Parallel Algorithms for Graphs
  • Appendix A: Written Exam
  • Appendix B: SLURM: A resource manager and job scheduler on clusters of machines
  • Appendix C: List of Figures
  • Appendix D: List of Tables
  • Appendix E: Index.

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