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Tutorial: VISION/HPC for Python-based Visual Parallel Computing

Language: English Quality: High Has Audio: true Source: Other Media: Flash Posted On: 22 Jun 09
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.

Tags: Screencast, IPython, Python, Language, Vision, HPC, series, osc.edu,     [SUGGEST  A  TAG]
     Series Listing
Part 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.
Part 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.
Part 3: An Example Using Matplotlib Library
In this segment, we discuss how to use the Matplotlib library to create interactive plots
Part 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.
Part 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.
Part 6: Creating customized nodes in VISION/HPC
In this segment, we explain how to create customized nodes.