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takes 1 00 milliseconds to produce a single item, the warm up time will be about a second; this is the latency incurred to produce one full item from the pipeline Once primed, however, new elements will be produced every 1 00 milliseconds Now let's look at an example of load imbalance Imagine a 3-stage pipeline Say that, the first stage takes, on average, 1 00 milliseconds to pro duce an item; the second stage takes, on average, 500 milliseconds to consume and produce an item; and, the final stage takes, on average, 50 milliseconds to consume and produce an item On a 1 6-core machine, a naIve implementation might assign 5 threads to each stage But this would perform very poorly: the first stage would complete in one-fifth the time of the second stage, and its 5 processors would then idle; and the third stage would spend most of its time blocked, waiting for the slow second stage to produce elements To see why this is true, imagine a pipeline with one thread dedicated to each of these stages The first element takes 1 00 mil liseconds to produce; until then, the second stage waits; it then consumes the element and produces one of its own, in 500 milliseconds elapsed time; in that amount of time the first stage has produced 5 more elements for it to work on; and the last stage had to wait 500 milliseconds to access some thing and will finish with it in a mere 50 milliseconds before having to wait 450 more for another There are many solutions to this problem, ranging from static allocation of threads to dynamic load balancing, much like the loop iteration division conundrum described earlier For illustration's sake, let's explore a static allocation that would help Say that, instead of 5 threads per stage, we vary the number per stage: the first stage gets 2 threads; the second stage gets 10 threads; and the last stage gets 1 thread (Yes, this fails to add up to 1 6-which is one of the drawbacks to static allocation-but let's continue) Now the pipeline is fairly balanced The first stage produces 2 new items every 1 00 milliseconds, for a production rate of 1 element/50 milliseconds; the second stage runs with 10 threads every 500 milliseconds which, on aver age, for a consumption and production rate of 1 element/50 milliseconds; and the last stage runs with a single thread with its ordinary consumption rate of 1 element/50 milliseconds Some degree of randomness and / or work variation can disrupt this.

pile benchmarking information from a selection of other companies, though the payroll manager can also arrange for the direct exchange of performance information with other companies A common result of such benchmarking activities is a new knowledge of additional actions the payroll department can take to improve its operations

9

programs Or, you can use threads to break one big task into multiple smaller pieces that can execute concurrently This is parallelism and is increasingly important as commodity hardware continues to increase the number of available processors Refer back to I, Introduction, for a detailed explanation of this taxonomy Threads are the fundamental units of schedulable concurrency on the Windows platform and are available to native and managed code alike This chapter takes a look at the essentials of scheduling and managing con currency on Windows using threads The APIs used to access threading in native and managed code are slightly different, but the fundamental archi tecture and OS support are the same But before we go into the details, let's precisely define what a thread is and of what it consists After that, we'll move on to how programs use them

10,001

A thread is in some sense just a virtual processor Each runs some pro gram's code as though it were independent from all other virtual proces sors in the system There can be fewer, equal, or more threads than real processors on a system at any given moment due (in part) to the multi tasking nature of Windows, wherein a user can run many programs at once, and the OS ensures that all such threads get a fair chance at running on the available hardware Given that this could be as much a simple definition of an OS process as a thread, clearly there has to be some interesting difference And there is (on Windows, at least) Processes are the fundamental unit of concurrency on many UNIX OSs because they are generally lighter-weight than Win dows processes A Windows process always consists of at least one thread that runs the program code itself But one process also may execute multi ple threads during the course of its lifetime, each of which shares access to a set of process-wide resources In short, having many threads in a single process allows one process to do many things at once The resources shared among threads include a single virtual memory address space, permitting threads to share data and communicate easily by reading from and writing to common addresses and objects in memory Shared resources also include

Summary

calculator or from published tables The probability statements are all based on the fact that (X )/( / n) is a legitimate random variable that will vary from sample to sample Now rearrange the inequality in the statement

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things associated with the Windows process, such as the handle table and security token information Most people get their first taste of threading by accident Developers use a framework such as ASPNET that calls their code on multiple threads simultaneously or write some GUI event code in Windows Forms, MFC, or Windows Presentation Foundation, in which there is a strong notion of particular data structures belonging to particular threads (We discuss this fact and its implications in 1 6, Graphical User Interfaces) These developers often learn about concurrency "the hard way" by accidentally writing unreliable code that crashes or by creating an unresponsive GUI by doing I / O on the GUI thread Faced with such a situation, people are quick to learn some basic rules of thumb, often without deeply under standing the reasons behind them This can give people a bad first impres sion of threads But while concurrency is certainly difficult, threads are the key to exploiting new hardware, and so it's important to develop a deeper understanding