Finding the Number of Processors in .NET

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The system affinity mask does indicate the number of processors on the system; all that is necessary is to count the number of bits that are set It is easier, however, to call GetSystemInfo, which returns a SYSTEM_INFO structure whose fields include the number of processors and the active processor mask, which is the same as the system mask A simple program and project, Version, on the book's Web site, displays this information along with the Windows version
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Hyperthreading and the Processor Count
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The Intel Pentium 4 and Xeon processors have a feature called HyperTreading whereby wait states during a thread's execution are used to run a different thread A second register file is used to support this feature, which is feasible because the x86 architecture has a relatively small number of registers A Xeon or other hyperthreading processor is regarded as a single processor by GetSystemInfo and GetProcessAffinityMask
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I/O Completion Ports
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14 describes I/O completion ports, which provide another mechanism to avoid thread contention by limiting the number of threads I/O completion ports allow a small number of threads to manage a large number of concurrent I/O operations Individual I/O operations are started asynchronously so that the operation is, in general, not complete when the read or write call returns However, as outstanding operations complete, data processing is handed off to one of a small number of worker threads 14 has an example using a server communicating with remote clients (Program 14-4)
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Performance Guidelines and Pitfalls
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Multiple threads can provide significant programming advantages, including simpler programming models and performance improvement However, there are several performance pitfalls that can have drastic and unexpected negative performance impact, and the impact is not always consistent on different computers, even when they are running the same Windows version Some simple guidelines, summarizing the experience in this chapter, will help to avoid these pitfalls Some of these guidelines are adapted from Butenhof's Programming with POSIX Pthreads, as are many of the designing, debugging, and testing hints in the next chapter
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Beware of conjecture and theoretical arguments about performance, which often sound convincing but can be wrong in practice Test the conjecture with a simple prototype, such as TimedMutualExclusion, or with alternative implementations of your application Test application performance on as wide a variety of systems as are available to you It is helpful to run with different memory configurations, processor types, Windows versions, and number of processors An application may perform very well on one system and then have extremely poor performance on a similar one; see the discussion of Program 9-1 Locking is expensive; use it only as required Hold (own) a mutex or CS only as long as required and no longer Setting the TimedMutualExclusion delay or sleep point parameters shows the impact of holding a lock for a long period of time Use distinct mutexes for distinct resources so that locking is as granular as possible In particular, avoid global locks High lock contention hinders good performance The greater the frequency of thread locking and unlocking, and the larger the number of threads, the greater the performance impact Performance degradation can be drastic and is not just linear in the number of threads CSs provide an efficient, lightweight locking mechanism when the number of contending threads is small, but mutexes sometimes provide better performance When using CSs in a performancecritical SMP application, tune performance with the CS spin counts Semaphores can reduce the number of active contending threads without forcing you to change your programming model SMP can cause severe, often unexpected, performance impacts in cases where you might expect improved performance Reducing contention and using thread affinity are techniques to maintain good performance Choices such as whether to use a signal or broadcast model, explained in 10, also affect performance significantly Investigate using commercially available profiling and performance analysis tools, which can help clarify the behavior of the threads in your program and locate time-consuming code segments
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Summary
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Synchronization can impact program performance on both single-processor and SMP systems; in some cases, the impact can be severe Careful program design and selection of the appropriate synchronization objects can help assure good performance This chapter has discussed a number of useful techniques and guidelines and has illustrated performance issues with a simple test program that captures the essential characteristics of many real programming situations
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