Name: [6237] unpingco
Member: 74 months
Authored: 40 videos
Description: Python for scientific and large-scale computing. ...

Visual Parallel Computing Using Python-based VISION/HPC [ID:743]

a series of video-tutorials by unpingco

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 programming task and the remote execution management. VISION/HPC is a Python-based, drag-and-drop visual-programming environment that reduces sophisticated programming tasks to dropping and connecting icons in a GUI flowchart. This is important for productivity since productivity is dominated by the time spent studying results versus the time spent writing maintainable code to generate those results. As a Python-based open-source package, it encapsulates scientific and parallel programming Python modules that are accessed through the visual interface. VISION/HPC runs on a local Windows PC and manages jobs on a remote backend. This means that the graphic intensive GUI runs on the local workstation and does not push individual pixels through a busy network connection.

VISION/HPC is downloadable and includes the documentation presented here.

Video Tutorials

1. Introducing VISION/HPC

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.

2. An Example Using the Imaging Library

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

3. An Example Using Matplotlib Library

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

4. Parallel Computing Using VISION/HPC

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.

5. Using the IPython Library

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.

6. Creating customized nodes in VISION/HPC

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

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