![]() ![]() #POINT CLOUD CONVEX HULL SCILAB HOW TO#Flat and hierarchical clustering algorithms are introduced for data exploration along with how to program these algorithms on computer clusters, followed by machine learning classification, and an introduction to graph analytics. In the second part, the book focuses on high-performance data analytics. This first part closes with the MapReduce (MR) model of computation well-suited to processing big data using the MPI framework. The common ring, torus and hypercube topologies of clusters are then explained and global communication procedures on these topologies are studied. The current version of the CGLAB toolbox provides a collection of functions, in particular Delaunay triangulations in 2D, 3D and nD space Convex hull in 2D and 3D Delaunay mesh generator in 2D space and many others. In the first part, the fundamental notions of blocking versus non-blocking point-to-point communications, global communications (like broadcast or scatter) and collaborative computations (reduce), with Amdalh and Gustafson speed-up laws are described before addressing parallel sorting and parallel linear algebra on computer clusters. 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.ĭivided into two parts the first part covers high performance computing using C++ with the Message Passing Interface (MPI) standard followed by a second part providing high-performance data analytics on computer clusters. ![]()
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |