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Another use for distributed systems is connecting older "legacy" systems within an organization to newer systems or to a Web interface The use of technologies like CORBA as middleware is very important in organizations with many systems written in many languages Java and CORBA can act as the glue that binds all these disparate machines and services together

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Regardless of the nature of the underlying data structure, if the data decomposition is the primary factor driving the solution to the problem, it serves as the organizing principle of the parallel algorithm When considering how to decompose the problem's data structures, keep in mind the competing forces

While readers should be familiar with at least the basics of the Java programming language and will likely be proficient with the language, there are some issues, delineated below, that all readers should be aware of

AmazonSimpleDB simpleDB = new AmazonSimpleDBClient(id, secret); DeleteAttributesRequest request = new DeleteAttributesRequest(); try { requestDomainName = "users"; requestItemName = "1234567"; requestWithAttribute( new Attr()WithName("tags")WithValue("App-Engine")); simpleDBDeleteAttributes(request);

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Flexibility The size and number of data chunks should be flexible to support the widest range of parallel systems One approach is to define chunks whose size and number are controlled by a small number of parameters These parameters define granularity knobs that can be varied to modify the size of the data chunks to match the needs of the underlying hardware (Note, however, that many designs are not infinitely adaptable with respect to granularity) The easiest place to see the impact of granularity on the data decomposition is in the overhead required to manage dependencies between chunks The time required to manage dependencies must be small compared to the overall runtime In a good data decomposition, the dependencies scale at a lower dimension than the computational effort associated with each chunk For example, in many finite difference programs, the cells at the boundaries between chunks, that is, the surfaces of the chunks, must be shared The size of the set of dependent cells scales as the surface area, while the effort required in the computation scales as the volume of the chunk This means that the computational effort can be scaled (based on the chunk's volume) to offset overheads associated with data dependencies (based on the surface area of the chunk)

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Efficiency It is important that the data chunks be large enough that the amount of work to update the chunk offsets the overhead of managing dependencies A more subtle issue to consider is how the chunks map onto UEs An effective parallel algorithm must balance the load between UEs If this isn't done well, some PEs might have a disproportionate amount of work, and the overall scalability will suffer This may require clever ways to break up the problem For example, if the problem clears the columns in a matrix from left to right, a column mapping of the matrix will cause problems as the UEs with the leftmost columns will finish their work before the others A row based block decomposition or even a block cyclic decomposition (in which rows are assigned cyclically to PEs) would do a much better job of keeping all the processors fully occupied These issues are discussed in more detail in the Distributed Array pattern

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