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Maple

grid computing

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.

grid screens

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 parallelisation commands.

Features list:

  • 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

Technical Requirements

  • Windows (including Vista)
  • Linux 32-bit
  • Linux 64-bit for Opteron (or equivalent)
  • Mac OSX 10.4 (PPC and Intel)
  • Sun Solaris


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Featured Downloads

Maple Grid Computing Toolbox datasheet
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Product Reviews

"Without the Maple software, we would have to spend weeks generating the equations of motion for every experiment. Then the chances that we did it right would basically be near zero. There would always be a mistake somewhere. It is very difficult to set up a dynamic motion model by hand."
- Jean-Claude PiedBeouf, Ph.D Manager of Robotics, Canadian Space Agency

"Its very good - highly accurate and easy to use. The speed of Maple allows me to change equations and quickly reintegrate them into the application, so more possibilities can be explored to achieve the precise effect desired."
Shawn Neely, Senior R & D Director for PDI/Dreamworks
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