7. Programming systems for GNU/Linux

This section deals with links to tutorials and documents for installing Linux on a PC, getting started with Linux, and then going a step further -- to optimize your PC for processing power, using multiple processors (Symmetric Muliti Processing - SMP); making a cheap, upgradeable Supercomputing Linux cluster and finally links to software to do parallel programming on Linux.

7.1. The GNU/Linux Workstation

As with most documentation related to GNU/Linux, the Linux Documentation project's home page is a priceless source. You might first want to read The Linux Installation HOWTO. For those who want to install Linux along with Windows might want to browse through The Linux + Windows HOWTO. When installing Linux make sure that you choose to install all documentation. After installing Linux, a good, comprehensive document to getting started with using Linux is The Rute Users Tutorial and Exposition which is a beginners guide to Linux and UNIX like systems. I'd like to give a less intimidating (size-wise) link to a small beginners guide, but U will find this useful after taking the plunge. You might also want to go through The Linux System Administrator's Guide and to check out The Linux Administration Made Easy (LAME) guide It attempts to describe day-to-day administration and maintenance issues commonly faced by Linux system administrators.

7.2. Parallel Processing and Symmetric Multiprocessing: Supercomputing

It is possible to get large volume number crunching without spending millions of rupees on a supercomputer. You only need to link together (by some high speed network) the requisite number of CPUs, with GNU/LINUX as the underlying OS. Add some freely available message passing software and a effective parallel processing number crunching machine is made. Such clusters are called "Beowulf clusters". The other advantages of such a cluster other than building costs is, up-gradation costs are minimal. The two best resources for Linux cluster builders are

These sites are upgraded frequently with useful information for cluster builders.

7.2.1. Parallel computing document links

You will also want to read this excellent article on Linux Clustering Software (and the large variety of links it provides) by Joe Greenseid. I hope to go through the links and include them subsequently in this HOWTO.

Other free document links for parallel processing are:

7.2.2. Parallel processing software for Linux

Now after reading the above documents, you have an idea of parallel processing. Parallel program libraries are the core of parallel processing on a Linux cluster. There are various free implementations of parallel processing libraries. Since parallel processing is all about performance, these libraries have some very nice functional tools to analyze your parallel program performance. Given below is a set of links to these parallel program libraries and tools.

  • Message Passing Interface: MPI is a standard specification of message passing libraries. The above document gives a lot of links to documents on the standard, etc.. A MPI implementation for Linux mpich is also available at that site. There are a lot of documents for Learning to use MPI .

  • Local Area Multicomputer - LAM: LAM (Local Area Multicomputer) is an MPI programming environment and development system for heterogeneous computers on a network. With LAM, a dedicated cluster or an existing network computing infrastructure can act as one parallel computer solving one problem. LAM features extensive debugging support in the application development cycle and peak performance for production applications. LAM features a full implementation of the MPI communication standard. You can download the sources (tar-zipped, rpm) or binaries from here A host of MPI tutorial links and also a `getting started with LAM' tutorial is available here

  • Parallel Virtual Machine : As the PVM home page describes, it is a software package that permits a heterogeneous collection of Unix and/or NT computers hooked together by a network to be used as a single large parallel computer. Thus large computational problems can be solved more cost effectively by using the aggregate power and memory of many computers. The software is very portable. The source, which is available free thru netlib, has been compiled on everything from laptops to CRAYs.

  • Ganglia: Ganglia is an open source cluster monitoring and execution environment developed at the University of California, Berkeley Computer Science Division. As the above link describes it, "Ganglia is as simple to install and use on a 16-node cluster as it is to use on a 512-node cluster as has been proven by its use on multiple 500+ node clusters". It not only can link nodes in a cluster, but also link clusters to other clusters.