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Agents Most programs are already coarsely decomposed into independent agents An agent in this context is a very abstract term, but the key attributes are: ( 1 ) state is mostly isolated within it from the outset, (2) its interactions with the world around it are asynchronous, and (3) it is generally loosely coupled with respect to peer agents There are many manifestations of agents in real-world systems, ranging from individual Web requests, a Windows Communication Foundation (WCF) service request, COM component call, some asynchronous activity a program has farmed off onto another thread, and so forth Moreover, some programs have just one agent: the program's entry point Tasks Individual agents often need to perform a set of operations at once We'll call these tasks Although a task shares many ideas with agents-such as being asynchronous and somewhat independent tasks are unique in that they typically share state intimately Many sequential client-side programs fail to recognize tasks are first class concepts, but doing so will become increasingly important as fine grained parallelism is necessary for multicore Many server-side programs also do not have a concept of tasks, because they already use large numbers of agents in order to expose enough latent concurrency to utilize the hardware This is OK so long as the number of active agents exceeds the number of available processors; as processor counts and the workloads a single agent is responsible for grow, this can become increasingly difficult to ensure Data Operations on data are often naturally parallel, so long as they are programmed such that the system is made available of latent concurrency This is called data parallelism Such operations might

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include transformations of data in one format into another, business intelligence analysis, encryption, compression, sorting, searching data for elements with certain characteristics, summarizing data for reporting purposes, rendering images, etc The more data there is, the more compute- and time-intensive these operations are They are typically leaf level, very fine grained, and, if expressed properly, help to ensure future scaling Many programs spend a large portion of their execution time working with data; thus, these operations are likely to grow in size and complexity as a program's requirements and data input evolves over time This taxonomy forms a nice hierarchy of concurrency, shown in Figure 1 1 While it's true that the clean hierarchy must be strictly broken in some cases (eg, a data parallel task may need to communicate with an agent), a clean separation is a worthy goal State isolation also is crucial to think about while architecting concurrent programs For example, it is imperative to strive for designs that lead to agents having state entirely isolated from one another such that they can remain loosely coupled and to ease the synchronization burden As finer grained concurrency is used, state is often shared, but functional concepts

such as immutability and purity become important: these disciplines help to eliminate concurrency bugs that can be extraordinarily difficult to track down and fix later The topics of state and synchronization are discussed at length in 2, Synchronization and Time What you'll find as you read the subsequent chapters in this book is that these terms and concepts are merely guidelines on how to create structured architecture in your program, rather than being concrete technologies that you will find in Windows and the NET Framework Several examples of agents were already given, and both task and data parallelism may take one of many forms today These ideas often map to work items executed in ded icated threads or a thread pool (see 7, Thread Pools), but this varies from one program to the next

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synchronization sensitive operations under the protection o f a critical region and not necessarily a restriction imposed by the mechanisms you will be using (Many encourage it, however, like C# and VB, which offer keyword support) It's possible, although often more difficult and much more error prone, to write a critical region that is more dynamic about entering and leaving regions

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BOOl f ( ) { if ( ) { EnterCrit i c a l Region ( ) ; s a ; I I some c ri t i c a l work ret u rn TRUE ; } ret u r n FALS E ; } void g O { if ( f ( { 5 1 ; II more c ri t i c a l wo rk leaveC r it i c a l Region ( ) ; } }

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