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DomainName (required) The name of the domain where the attribute(s) will be storedThis parameter must be the name of a domain that has already been created ItemName (required) The unique key with which a list of attributes will be associatedThis parameter is passed as a list, each with an associated sub-list of attributes This can be a new ItemName for the purposes of an insert or an existing ItemName for the purposes of an updateThe maximum length of this parameter is 1,024 bytesThe maximum number of ItemName parameters for a single request is 25 AttributeName (required) The name of the attribute, similar to a column name, passed as a list in combination with an AttributeValue and optionally a Replace flagThe minimum length is 1 and the maximum length of this parameter is 1,024 bytes AttributeValue (required) The value to be stored, passed as a list in combination with an AttributeName and optionally a Replace flagThis value must be present but it can have zero length (the empty string)The maximum length of this parameter is 1,024 bytes

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Figure 615 The message-passing block from Fig 613 and Fig 614, but with more careful synchronization management (pairwise synchronization)

// Try block can generate exceptions try { // generate an exception } catch (SomeException some) { // handle some exception

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Replace (optional) The flag that specifies whether to replace existing attribute values or merely add a new attribute valueThis parameter is passed as a list in combination with an AttributeName and an AttributeValue If this parameter is not given, a default value of false is used

} finally { // clean up after try block, regardless of any // exceptions that are thrown }

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Consider the medical imaging problem described in Sec 313 In this application, a point inside a model of the body is selected randomly, a radioactive decay is allowed to occur at this point, and the trajectory of the emitted particle is followed To create a statistically significant simulation, thousands if not millions of trajectories are followed In a data based decomposition of this problem, the body model is the large central data structure around which the computation can be organized The model is broken into segments, and one or more segments are associated with each processing element The body segments are only read, not written, during the trajectory computations, so there are no data dependencies created by the decomposition of the body model After the data has been decomposed, we need to look at the tasks associated with each data segment In this case, each trajectory passing through the data segment defines a task The trajectories are initiated and propagated within a segment When a segment boundary is encountered, the trajectory must be passed between segments It is this transfer that defines the dependencies between data chunks On the other hand, in a task based approach to this problem (as discussed in the Task Decomposition pattern), the trajectories for each particle drive the algorithm design Each PE potentially needs to access the full body model to service its set of trajectories In a shared memory environment, this is easy because the body model is a read only data set In a distributed memory environment, however, this would require substantial startup overhead as the body model is broadcast across the system This is a common situation in parallel programming: Different points of view lead to different algorithms with potentially very different performance characteristics The task based algorithm is simple, but it only works if each processing element has access to a large memory and if the overhead incurred loading the data into memory is insignificant compared to the program's runtime An algorithm driven by a data decomposition, on the other hand, makes efficient use of memory and (in distributed memory environments) less use of network bandwidth, but it incurs more communication overhead during the concurrent part of computation and is significantly more complex Choosing which is the appropriate approach can be difficult and is discussed further in the Design Evaluation pattern.

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2714 Causes of Exceptions Despite the best-laid plans, no application is foolproof and exceptions will be encountered at runtime This will, it is hoped, occur during debugging, rather than exceptions being accidentally discovered by users By catching and handling exceptions, applications will be robust enough to recover, and if not, to retire gracefully with an appropriate message In networking, the most common cause of exceptions is related to the state of the network connection Users connect to network services over a variety of communication mechanisms, of varying degrees of speed and quality When a network loses its connection to the Internet, or that connection becomes highly congested with traffic, it may be impossible to connect to a particular host and thus to its network services Or the user may be behind a firewall, preventing a direct connection from being established In our experience, these are the two most common causes of exceptions in network applications The next most likely cause is a security restriction, either imposed by the browser on applets or by a Java security policy or security manager When writing applets or software that uses security (such as distributed clients and servers), programmers may want to consider trying to catch any Security Exception that is thrown Though not mandatory, in that the compiler does not enforce it, it may be advantageous to do so if users are likely to change their security settings or run software within restricted environments However, a SecurityException is thrown only if code attempts to do something prohibited by the JVM external security measures such as firewalls will simply result in an inability to connect to certain hosts

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