Disco is an open-source implementation of the Map-Reduce framework for distributed computing. As the original framework, Disco supports parallel computations over large data sets on unreliable cluster of computers.
"For a while now, IBM has had multiple and competing tools for managing AIX and Linux clusters for its supercomputer customers and yet another set of tools that were used for other HPC setups with a slightly more commercial bent to them. But Big Blue has now cleaned house, killing off its closed-source Cluster Systems Management (CSM) tool and tapping its own open source Extreme Cluster Administration Toolkit (known as xCAT) as its replacement."
Building and Promoting a Linux-based Operating System to Support Virtual Organizations for Next Generation Grids (2006-2010). The emergence of Grids enables the sharing of a wide range of resources to solve large-scale computational and data intensive problems in science, engineering and commerce. While much has been done to build Grid middleware on top of existing operating systems, little has been done to extend the underlying operating systems to enablee and facilitate Grid computing, for example by embedding important functionalities directly into the operating system kernel.
SystemImager is software which automates Linux installs, software distribution, and production deployment. SystemImager makes it easy to do automated installs (clones), software distribution, content or data distribution, configuration changes, and operating system updates to your network of Linux machines. You can even update from one Linux release version to another! It can also be used to ensure safe production deployments. By saving your current production image before updating to your new production image, you have a highly reliable contingency mechanism. If the new production enviroment is found to be flawed, simply roll-back to the last production image with a simple update command! Some typical environments include: Internet server farms, database server farms, high performance clusters, computer labs, and corporate desktop environments.
Modern graphics processing units (GPUs) contain hundreds of arithmetic units and can be harnessed to provide tremendous acceleration for many numerically intensive scientific applications. The key to effective utilization of GPUs for scientific computing