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extremely rapid context switching, making it practical to use in a far wider range of latency hiding situations Other systems do not provide good support for multiple UEs per PE For example, an MPP system with slow context switching and/or one processor per node might run much better when there is only one UE per PE The second factor is whether the design can make good use of multiple UEs per PE For example, if the design involves communication operations with high latency, it might be possible to mask that latency by assigning multiple UEs to each PE so some UEs can make progress while others are waiting on a high latency operation If, however, the design involves communication operations that are tightly synchronized (for example, pairs of blocking send/receives) and relatively efficient, assigning multiple UEs to each PE is more likely to interfere with ease of implementation (by requiring extra effort to avoid deadlock) than to improve efficiency On the target platform, will the time spent doing useful work in a task be significantly greater than the time taken to deal with dependencies A critical factor in determining whether a design is effective is the ratio of time spent doing computation to time spent in communication or synchronization: The higher the ratio, the more efficient the program This ratio is affected not only by the number and type of coordination events required by the design, but also by the characteristics of the target platform For example, a message passing design that is acceptably efficient on an MPP with a fast interconnect network and relatively slow processors will likely be less efficient, perhaps unacceptably so, on an Ethernet connected network of powerful workstations Note that this critical ratio is also affected by problem size relative to the number of available PEs, because for a fixed problem size, the time spent by each processor doing computation decreases with the number of processors, while the time spent by each processor doing coordination might stay the same or even increase as the number of processors increases.

Five packages deal with RMI The two most commonly used are javarmi and javarmiserver, but it's important to be aware of the functionality provided by the remaining three 1 javarmi defines the Remote interface, classes used in RMI, and a number of exceptions that can occur at runtime 2 javarmiactivation introduced in the Java 2 platform, this package supports "activation" of remote services Activation allows a service to be started on demand, rather than running continually and consuming system resources 3 javarmidgc provides an interface and two classes to support distributed garbage collection Just as objects can be garbage-collected by the JVM, distributed objects may be collected when clients no longer maintain a reference to them 4 javarmiregistry provides an interface to represent an RMI registry and a class to locate an existing registry or launch a new one 5 javarmiserver provides interfaces and classes related to RMI servers, as well as a number of server-specific exceptions that may be thrown at runtime 11911 Package javarmi There are one interface, three classes, and a large number of exception classes defined by this package Each is discussed separately Remote Interface The javarmiRemote interface is unusual, in that it does not define any methods for implementing classes It is, instead, used as a means of identifying a remote service Every RMI service interface will extend the Remote interface Being an interface, it cannot be instantiated (only a class that implements an interface may be instantiated) However, an implementing class may be cast to a Remote instance at runtime When creating a service that extends the javarmiRemote interface, you should be aware that methods must declare a throws clause, listing at least javarmiRemoteException, and (optionally) any application-specific exceptions This is not declared in the API documentation for the Remote interface, but is a condition imposed by the rmic tool Failure to adhere to this.

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There are quite a few limitations on what you are allowed to do with SimpleDB Most of these are size and quantity restrictionsThere is an underlying philosophy that small and quickly serviced units of work provide the greatest opportunity for load balancing and maintaining uniform service levelsAWS maintains a current listing of the service limitations within the latest online SimpleDB Developer Guide at the AWS websiteAt the time of this writing, the limits are as follows:

Keeping these characteristics of the target platform in mind, we can evaluate the design along the three dimensions of flexibility, efficiency, and simplicity Flexibility It is desirable for the high level design to be adaptable to a variety of different implementation requirements, and certainly all the important ones The rest of this section provides a partial checklist of factors that affect flexibility

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