The chief impediment to widespread usage of parallel computing is the difficulty in programming HPCs. Furthermore, most users work from a Windows PC so that learning UNIX as a prerequisite to parallel programming is a further obstruction. What is needed is a smooth workflow that simplifies both the p...

In this segment, we explain how to create customized nodes.

This segment discusses the MEC and MECXLocal nodes in the IPython library to quickly prototype a parallel computation without having a prior connection to a backend cluster.

This segment discusses how VISION/HPC works with the underlying IPython library for parallel computation to compute the fractal image in the built-in demo.

In this segment, we discuss how to use the Matplotlib library to create interactive plots

In this segment, we discuss the Imaging Library and how to use it to create a network to load, review, and manipulate images.

In this seminal segment, we discuss getting started with VISION/HPC, how to use the associated documentation, how to draw and connect nodes to create networks, moving and connecting nodes, basic terminology and the main interface, and the interpreter window.

In this final segment, we highlight concepts important for new scientific Python programmers, especially those coming from a Matlab environment. We discuss effective use of Python dictionaries and the timeit profiling module. We also discuss the defaultdict object for quick and easy database creation...

In this segment, we show how the intensive least-the in computation can be accelerated using the parallel computing facilities of IPython we have considered so far.

In this segment, we show how to compute the least-median estimate using the sample data set and Python generators.

In this segment, we pull together everything we have learned so far to consider robust regression methods that can easily handle the presence of outliers in the data. Here, we discuss what these regression methods are and how we can apply what we have learned so far

In this segment, we discuss the push, pull, and push_function, features of IPython that allow finer control over individual namespaces of remote nodes and over the computations that run on them.