Scyld clustering provides a facile solution for anyone executing jobs that involve either a large number of computations or large amounts of data (or both). It is ideal for both large, monolithic, parallel jobs and for many normal-sized jobs run many times (such as Monte Carlo type analysis).
The increased computational resource needs of modern applications are frequently being met by Scyld clusters in a number of domains, including:
Computationally-Intensive Activities — Optimization problems, stock trend analysis, financial analysis, complex pattern matching, medical research, genetics research, image rendering
Scientific Computing / Research — Engineering simulations, 3D-modeling, finite element analysis, computational fluid dynamics, computational drug development, seismic data analysis, PCB / ASIC routing
Large-Scale Data Processing — Data mining, complex data searches and results generation, manipulating large amounts of data, data archival and sorting
Web / Internet Uses — Web farms, application serving, transaction serving, data serving
These types of jobs can be performed many times faster on a Scyld cluster than on a single computer. Increased speed depends on the application code, the number of nodes in the cluster, and the type of equipment used in the cluster. All of these can be easily tailored and optimized to suit the needs of your applications.