Engineers looking to tap into high-performance computing
(HPC) horsepower to help solve their complex mathematical problems now have a
new option with the latest version of Maplesoft's Maple Grid Computing Toolbox.
The Maple 14 release of the Grid Computing Toolbox provides
built-in integration with Windows HPC Server, including Windows HPC Server 2008
R2. The platform connects directly to a user's Windows HPC Server cluster,
simplifying installation, configuration and operation of the grid. It also
integrates with the Windows HPC Server tool chain to handle administrative
tasks such as job scheduling, load balancing and usage monitoring. The Maple 14
Grid Computing Toolbox also uses the standard message passing interface (MPI)
protocol for efficient communication between nodes in the grid, and easy
integration with tools that support this protocol.
The Maple Grid Computing Toolbox allows users to run Maple
computations in parallel, taking advantage of the hardware resources they have
available. The toolbox essentially lets engineers distribute computations
across the nodes of a network of workstations, a supercomputer or the CPUs of a
multiprocessor computer. By doing so, engineers can handle computational
problems that were not doable on a single machine because of memory limitations
or because the workload would simply take too long.
The Grid Computing Toolbox is available in two
different versions: The Personal
Edition supports up to eight CPUs in the cluster while the Cluster Edition supports an unlimited
number of CPUs in the cluster.
A new service lets engineers and orthopedic surgeons design and 3D print highly accurate, patient-specific, orthopedic medical implants made of metal -- without owning a 3D printer. Using free, downloadable software, users can import ASCII and binary .STL files, design the implant, and send an encrypted design file to a third-party manufacturer.
For industrial control applications, or even a simple assembly line, that machine can go almost 24/7 without a break. But what happens when the task is a little more complex? That’s where the “smart” machine would come in. The smart machine is one that has some simple (or complex in some cases) processing capability to be able to adapt to changing conditions. Such machines are suited for a host of applications, including automotive, aerospace, defense, medical, computers and electronics, telecommunications, consumer goods, and so on. This discussion will examine what’s possible with smart machines, and what tradeoffs need to be made to implement such a solution.