Information

• book information on Springer web site
• 30+ C++/MPI programs, 40+ exercises, and a 3-hour written exam with the complete solution
• Online: Amazon.fr, Amazon.com, Amazon.co.jp, etc.
  
• on Google Play
• on Google book
• Promotional video
• Table of contents in Chinese (courtesy of Junlin Yao)

Errata

Table of Contents

• Preface
• Part I. 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 II. High Performance Computing (HPC) for Data Science (DS)
• Partition-based clustering with k-means
• Hierarchical clustering
• Supervised learning: Practice and theory of classification with the k-NN rule
• Fast approximate optimization in high dimensions with core-sets and fast dimension reduction
• Parallel algorithms for graphs
• Appendices
• Written exam
• SLURM: A resource manager & job scheduler on clusters of machines

Extra materials

• Part I. High Performance Computing (HPC) with the Message Passing Interface (MPI)
• A glance at High Performance Computing (HPC)
  Amdahl.gnuplot
• Introduction to MPI: The Message Passing Interface:
  MPIBlockingCommunication.cpp MPINonblockingCommunication.cpp MPICommunicatorRemoveFirstProcess.cpp MPICommunicatorSplitProcess.cpp MPIHelloWorld.cpp MPICBindingExample.c MPIBoostBindingExample.cpp MPIBoostBindingExample.cpp MPIOpenMPExample.cpp MPIHelloWorld.py MPIMatrixVectorMultiplication.cpp MPIFactorialReduce.cpp MPIMinimumReduce.cpp MPIMonteCarloPi.cpp SequentialVolumeUnionSpheres.cpp MPIVolumeUnionSpheres.cpp MPIPiApproximationHole.cpp (incomplete, exercise)
• Topology of interconnection networks
  GrayString.cpp GraySTL.cpp MPIBroadcastRing.cpp MPIScatteringRing.cpp MPIBroadcastHypercube.cpp
• Parallel Sorting
  SequentialQuickSort.cpp OddEvenSort.cpp
• Parallel linear algebra
  ExampleLinearRegression.scilab
• The MapReduce paradigm
• Part II. High Performance Computing (HPC) for Data Science (DS)
• Partition-based clustering with k-means
  Example-kMeans.R
  
  kmeansLloydProcessing.pde in processing
• Hierarchical clustering
• Supervised learning: Practice and theory of classification with the k-NN rule
  
  NNDecisionBoundary.pde in processing
• Fast approximate optimization in high dimensions with core-sets and fast dimension reduction
• Parallel algorithms for graphs
  
  ParallelDenseSubgraph.pde
  SequentialDenseSubgraph.pde
  using auxiliary file graph.java
  in processing