GigaSpaces Enterprise Edition Parallel Processing

{color:orange}Cut Complexity and Boost Performance with Parallel Data Processing{color}

GigaSpaces leverages its grid-based architecture to offer a solution to both data access and data processing. Combining distributed caching with a parallel processing engine, GigaSpaces enables real-time analytic applications to achieve better performance, scalability, simplicity, and consistency throughout all application elements. In addition, GigaSpaces saves the cost, complexity, and support effort normally involved when combining two technologies from two different products.

For example, a J2EE application that has to perform complex tasks often suffers from the lack of capabilities to utilize existing system resources, both internal (within the container) and external (within the network environment). GigaSpaces provides a "light" GRID engine specifically designed to perform the following tasks:

Using the GigaSpaces Enterprise Edition, J2EE applications can utilize distributed system resources such as CPU and Memory. This improves the scalability and performance of these applications. The space coordinates and synchronizes the access to the distributed resources to ensure reliable processing

GigaSpaces parallel-processing engine enables J2EE developers to improve application performance. When J2EE logical components require several tasks to be processed in parallel, and the results to be collected as they arrive, GigaSpaces tools enable reliable, recoverable, and guaranteed execution of the tasks in parallel.

{color:orange}J2EE Parallel Processing Example{color}


In the above figure, a J2EE application performs a parallel task using a Master/Worker pattern. In this case, the J2EE session bean is the Master application and a set of generic workers represents the processing units. The session bean performs a parallel JOB by writing a set of tasks into the space. Workers take these tasks in their idle time and execute them in the worker environment. In this way, the workers utilize their machine CPU and memory resources on behalf of the Master Session Bean. Upon completion, the workers write the result back to the space. The Master takes the results of the individual tasks and compiles them into a unified JOB result, which is sent back to application when all the tasks are completed.

{quote}This thread was imported from the previous forum. For your reference, the original is [available here|]{quote}

asked 2006-02-25 16:40:04 -0500

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updated 2013-08-08 09:52:00 -0500

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