SCAI's Tuplespace software products are a proven, easy-to-use approach to building distributed applications. Our products are validated, promoted, and jointly marketed with several computer vendors including IBM, HP, and Apple.
Scientific Computing Associates - Parallel Processing, Blade, Grid, Cluster configurations
Hundreds of firms around the world including Lehman Brothers, Amerada Hess, Kodak, Merrill Lynch, Motorola and thousands of users benefit from our technology on networks, clusters, and multiprocessors.

New from SCAI

SCAI announces NetWorkSpaces™ with Sleigh™, a powerful software package that makes it easy to use clusters from within scripting languages like R, Python, and Matlab. NetWorkSpaces uses globally-shared workspaces to develop and run distributed and parallel script applications.

NetWorkSpaces is the latest addition to SCAIís family of software products for high-performance parallel and distributed computing. NetWorkSpaces is easy to learn and use, which increases user productivity in implementing and maintaining distributed applications.

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About Us

Since 1980, SCAI has pioneered the commercial use of parallel and distributed computing. With its introduction of Linda, the company was the first to offer a cost-effective, packaged tuplespace technology product to harness the potential of supercomputing. The company's expertise and ongoing research has resulted in worldwide clients, government contracts, and development and marketing partnerships with numerous companies. SCAI also offers a variety of training and support options.

Complete Projects Faster

SCAI's Linda Virtual Machines suite of software tools provides an enterprise-wide framework for building distributed systems across a network. We can show you how to leverage your existing computer infrastructure or server clusters and save hundreds of thousands of dollars in hardware expenses. We can also help you reduce development costs and speed delivery, while allowing your software developers to focus on their code--not on time-consuming and expensive systems integration.

 
 
 
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