1. Cluster Computing and Genetic Algorithms With ClusterKnoppix David Tabachnick

2. What is a Cluster? A Cluster is… A group of connected computers, acting as one to calculate computationally difficult problems. A Cluster is… Made up of a server, nodes, and networking hub(s).

3. Example of a Fully Developed Cluster

4. Basics of Clustering  Server is the head of the cluster.  Nodes are all connected via hub.  Server is started first. The nodes are then powered on, and they look to the server for instructions.  The cluster is both scalable and highly available.  The cluster can be taken down and restarted quickly and easily.

5. Methods to Clustering  Load-balancing is done on the server by a software package, OpenMosix.  This results in breaking programs into processes that can be sent to the nodes for processing.  No special programming methods or languages must be used.

6. Features of this Cluster  Live Install with configuration scripts  Remote access for authorized users  High scalability  Extremely secure  Local storage on the server and external storage for redundant data backup  Will run LISP

7. Genetic Algorithms on a Cluster  Server performs the string manipulation and sends the matrix calculations to the nodes.  Each node is given one fitness function to calculate.  Upon completion, a node will write back the resultant function and remain idle.  ≈80% of the calculations take place on the nodes.

8. Genetic Algorithm Clustering

9. Conclusion  This cluster…  Achieved relatively high performance when compared to other clusters of similar size and scope.  Was designed with computing genetic algorithms in mind.  Was built to be scalable, flexible, and easily accessible.