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In this section, we define some terms that are frequently used throughout the pattern language Additional definitions can be found in the glossary Task The first step in designing a parallel program is to break the problem up into tasks A task is a sequence of instructions that operate together as a group This group corresponds to some logical part of an algorithm or program For example, consider the multiplication of two order N matrices Depending on how we construct the algorithm, the tasks could be (1) the multiplication of subblocks of the matrices, (2) inner products between rows and columns of the matrices, or (3) individual iterations of the loops involved in the matrix multiplication These are all legitimate ways to define tasks for matrix multiplication; that is, the task definition follows from the way the algorithm designer thinks about the problem Unit of execution (UE) To be executed, a task needs to be mapped to a UE such as a process or thread A process is a collection of resources that enables the execution of program instructions These resources can include virtual memory, I/O descriptors, a runtime stack, signal handlers, user and group IDs, and access control tokens A more high level view is that a process is a "heavyweight" unit of execution with its own address space A thread is the fundamental UE in modern operating systems A thread is associated with a process and shares the process's environment This makes threads lightweight (that is, a context switch between threads takes only a small amount of time) A more high level view is that a thread is a "lightweight" UE that shares an address space with other threads We will use unit of execution or UE as a generic term for one of a collection of possibly concurrently executing entities, usually either processes or threads This is convenient in the early stages of program design when the distinctions between processes and threads are less important Processing element (PE) We use the term processing element (PE) as a generic term for a hardware element that executes a stream of instructions The unit of hardware considered to be a PE depends on the context For example, some programming environments view each workstation in a cluster of SMP workstations as executing a single instruction stream; in this situation, the PE would be the workstation A different programming environment running on the same hardware, however, might view each processor of each workstation as executing an individual instruction stream; in this case, the PE is the individual processor, and each workstation contains several PEs.

must call when the button is pressed, in this case the doAction() function We have used partial function application to ensure that the rst argument given to the doAction() function is a string that indicates which button called it so that doAction() is able to decide what action to perform

Entity Problems 171 Too Few Entities 171 Too Many Entities 174 Attribute Problems 176 Single Attributes Contain Different Data 176 Incorrect Data Types 178 Relationship Problems 182 One-to-One Relationships 182 Many-to-Many Relationships 184 Summary 185

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Coroutines are functions whose processing can be suspended and resumed at speci c points So, typically, a coroutine will execute up to a certain statement, then suspend execution while waiting for some data At this point other parts of the program can continue to execute (usually other coroutines that aren t suspended) Once the data is received the coroutine resumes from the point it was suspended, performs processing (presumably based on the data it got), and possibly sending its results to another coroutine Coroutines are said to have multiple entry and exit points, since they can have more than one place where they suspend and resume Coroutines are useful when we want to apply multiple functions to the same pieces of data, or when we want to create data processing pipelines, or when we want to have a master function with slave functions Coroutines can also be used to provide simpler and lower-overhead alternatives to threading A few coroutine-based packages that provide lightweight threading are available from the Python Package Index, pypipythonorg/pypi

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In Python, a coroutine is a function that takes its input from a yield expression It may also send results to a receiver function (which itself must be a coroutine) Whenever a coroutine reaches a yield expression it suspends waiting for data; and once it receives data, it resumes execution from that point A coroutine can have more than one yield expression, although each of the coroutine examples we will review has only one

If we want to perform a set of independent operations on some data, the conventional approach is to apply each operation in turn The disadvantage of this is that if one of the operations is slow, the program as a whole must wait for the operation to complete before going on to the next one A solution to this is to use coroutines We can implement each operation as a coroutine and then start them all off If one is slow it won t affect the others at least not until they run out of data to process since they all operate independently Figure 82 illustrates the use of coroutines for concurrent processing In the gure, three coroutines (each presumably doing a different job) process the same two data items and take different amounts of time to do their work In the gure, coroutine1() works quite quickly, coroutine2() works slowly, and coroutine3() varies Once all three coroutines have been given their initial data

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Step 1 2 3 4 5 6 7 8 9 10 11 12 coroutineNclose() Action Create coroutines coroutine1send("a") coroutine2send("a") coroutine3send("a") coroutine1send("b") coroutine2send("b") coroutine3send("b")

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