Home : All Products : Maple Professional Toolbox Series : Grid Computing Toolbox

The Grid Computing Toolbox will enable you to run Maple computations in parallel, taking advantage of all the hardware resources that are available to you, cutting down on processing time and enabling applications that were not possible before. The Maple Grid Computing Toolbox allows you to distribute computations across the nodes of a network of workstations, a supercomputer or across the CPUs of a multiprocessor machine. This allows you to handle problems that are not tractable on a single machine because of memory limitations or because it would simply take too long. The Grid Computing Toolbox is very easy to set up. Start a server process on each machine on a network and the grid will self-assemble as each node automatically detects the other nodes that are present. The Grid Computing Toolbox also integrates into existing job scheduling systems like PBS. The Grid Computing Toolbox includes a personal grid server, allowing you to simulate a grid with any number of nodes on your desktop machine. You can develop and test your parallel applications before running them on the real grid. In order to perform distributed computations, the Grid Computing Toolbox offers an MPI-like message passing API as well as a set of high-level parallelization commands. Availability The Grid Computing Toolbox is available in two different versions: The Personal Edition supports up to 8 CPUs in the cluster. The Cluster Edition supports an unlimited number of CPUs in the cluster. Both versions are available from the Maplesoft Web Store. View system requirements Note that a Maple license is required for each node in the cluster. Volume pricing is available for the node installations of Maple. Contact Maplesoft for details. Key Features Distribute Maple computations across a network Self-assembling grid in local networks Simple to use interactive interface for launching parallel jobs Personal Grid Server to develop and test applications on your desktop Integration with PBS and other job scheduling systems MPI-like message passing API (send, receive, etc) Automatic deadlock detection and recovery High-level parallelization commands (map, seq, etc) Generic, parallel divide-and-conquer algorithm Many documented examples Supports heterogenous networks Access to all the computational power of Maple