There are dozens of active shared computing system projects, each with its own networks and computational tasks. Some of these networks overlap -- it's possible for a user to participate in more than one network, though it does mean that different projects have to divvy up the idle resources. As a result, each individual task takes a little longer.
One example of a shared computer system is the Grid Laboratory of Wisconsin (GLOW). The University of Wisconsin-Madison uses GLOW for multiple projects, which in some ways sets it apart from most shared computing systems. One project uses the GLOW network to study the human genome. Another takes advantage of GLOW's resources to research potential treatments for cancer. Unlike the shared computing systems that are dedicated to a single task, GLOW can accommodate multiple projects.
The software that makes GLOW possible is called Condor. It's Condor's job to seek out idle processors within the GLOW network and use them to work on individual projects. When one project is inactive, Condor borrows its resources for the other projects. However, if any previously inactive project comes back online, Condor releases the respective computers' processors.
Some other shared computing systems include:
- SETI@home: A project that analyzes data from radio telescopes in search of intelligent extraterrestrial life.
- Africa@home: This project dedicates computer power to research programs designed to improve the quality of life in Africa with a focus on malaria control initiatives.
- Proteins@home, Predictor@home, Rosetta@home and Folding@home: Each of these projects studies proteins in various ways.
- Einstein@home, Cosmology@home, Milkyway@home and Orbit@home: These projects study astronomical data.
Other projects study everything from the physics of fluid dynamics to simulated nanotechnology environments.
So shared computing systems can be really useful, but are there any dangers? Read on, if you aren't scared.