Further Readings in Java

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435 Further Readings
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ABCL was among the first concurrent object-oriented languages to offer Futures as a language construct See: Yonezawa, Akinori, and Mario Tokoro Object-Oriented Concurrent Programming, MIT Press, 1988 Futures are known as wait-by-necessity constructions in Eiffel// (a parallel extension to Eiffel) See: Caromel, Denis, and Yves Roudier "Reactive Programming in Eiffel//", in Jean-Pierre Briot, JeanMarc Geib and Akinori Yonezawa (eds) Object Based Parallel and Distributed Computing, LNCS 1107, Springer Verlag, 1996 Futures and related constructs in the Scheme and Multilisp programming languages are described in: Dybvig, R Kent and Robert Hieb "Engines from Continuations", Computer Languages, 14(2):109123, 1989 Feeley, Marc An Efficient and General Implementation of Futures on Large Scale Shared-Memory Multiprocessors, PhD Thesis, Brandeis University, 1993 Additional techniques associated with completion callbacks in networking applications are described in: Pyarali, Irfan, Tim Harrison, and Douglas C Schmidt "Asynchronous Completion Token", in Robert Martin, Dirk Riehle, and Frank Buschmann (eds), Pattern Languages of Program Design, Volume 3, Addison-Wesley, 1999 The Null Object pattern is often useful for simplifying callback designs in which clients do not always require callback messages See: Woolf, Bobby "Null Object", in Robert Martin, Dirk Riehle, and Frank Buschmann (eds), Pattern Languages of Program Design, Volume 3, Addison-Wesley, 1999
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44 Parallel Decomposition
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Parallel programs are specifically designed to take advantage of multiple CPUs for solving computation-intensive problems The main performance goals are normally throughput and scalability
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the number of computations that can be performed per unit time, and the potential for improvement when additional computational resources are available However, these are often intertwined with other performance goals For example, parallelism may also improve response latencies for a service that hands off work to a parallel execution facility Among the main challenges of parallelism in the Java programming language is to construct portable programs that can exploit multiple CPUs when they are present, while at the same time working well on single processors, as well as on time-shared multiprocessors that are often processing unrelated programs Some classic approaches to parallelism don't mesh well with these goals Approaches that assume particular architectures, topologies, processor capabilities, or other fixed environmental constraints are ill suited to commonly available JVM implementations While it is not a crime to build run-time systems with extensions specifically geared to particular parallel computers, and to write parallel programs specifically targeted to them, the associated programming techniques necessarily fall outside the scope of this book Also, RMI and other distributed frameworks can be used to obtain parallelism across remote machines In fact, most of the designs discussed here can be adapted to use serialization and remote invocation to achieve parallelism over local networks This is becoming a common and efficient means of coarse-grained parallel processing However, these mechanics also lie outside the scope of this book We instead focus on three families of task-based designs, fork/join parallelism, computation trees, and barriers These techniques can yield very efficient programs that exploit multiple CPUs when present, yet still maintain portability and sequential efficiency Empirically, they are known to scale well, at least up through dozens of CPUs Moreover, even when these kinds of task-based parallel programs are tuned to maximally exploit a given hardware platform, they require only minor retunings to maximally exploit other platforms As of this writing, probably the most common targets for these techniques are applications servers and compute servers that are often, but by no means always, multiprocessors In either case, we assume that CPU cycles are usually available, so the main goal is to exploit them to speed up the solution of computational problems In other words, these techniques are unlikely to be very helpful when programs are run on computers that are already nearly saturated
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