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CHAPTER 7 EFFICIENT CODE
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to that of using traffic lights. When using traffic lights, traffic is interrupted because cars have to stop when the lights are red. This causes an abrupt behavior to traffic flow, and if badly designed, causes traffic to deadlock. However, if traffic lights are planned effectively, for example, using the so-called green wave (where lights are synchronized to green to allow fairly free flow of vehicles), then traffic will be smoother. This is the challenge for programmers when using synchronization.
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A Simple Example of Synchronization
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The simplest of all synchronization techniques is to use the lock keyword. The lock keyword synchronizes access to a block of code as illustrated by the following example: class ConcurrentAccess { private int _a; public void AssignVariable( int a) { lock( this) { _a = a; } } } Here the method AssignVariable includes the keyword lock, which has a single parameter, this. This results in a synchronization block being defined between the curly brackets. A single thread will only execute code between the curly brackets. Other threads will have to wait until the single thread exits the code block. Synchronization is relative to the parameter associated with the lock keyword. Consider the following source code: class ConcurrentAccess { private int _a; public void AssignVariable( int a) { lock( this) { _a = a; } } public void AssignAndIncrement( int a) { lock( this) { _a = a; _a ++; } } } In this modified implementation, an additional method, AssignAndIncrement, was added that also used the lock keyword. Notice how both lock examples use as a parameter this. This means that regardless of which method is called, the code bounded by the lock keyword will only ever have a single thread accessing the data. This is absolutely vital, because it would be entirely undesirable to have two threads assigning the variable _a.
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CHAPTER 7 EFFICIENT CODE
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Now it should become obvious what is problematic is that threads might have to wait excessively to manipulate the data. Or worse, the code deadlocks and doesn t execute any further because of locks are being held by threads that are attempting to access synchronized code. Immutable objects are a huge advantage in that an individual immutable object requires no synchronization. This is because an immutable object can t be modified, and therefore you don t have to fear that another thread will update the state of an object. However, immutable objects still require some locks since, after all, the reference to an immutable object must be established, and therefore a lock is required. Hence, immutable objects reduce dramatically the number of locks required, but don t eradicate them.
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Don t Instantiating and Copying Make an Application Slower
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Assuming that you accept immutable objects are faster from a synchronization point of view, there is still a concern regarding resources. Where immutable objects can be slower is the constant thrashing of memory due to allocation and freeing of objects. That thinking, while correct in theory, is incorrect in practice, or at least mostly incorrect. In the past, memory allocations were considered very expensive. When using C and C++, the buffer is allocated from a subdivided heap. When allocating a buffer in C and C++, the memory allocation routines search a list of pointers and check whether a heap subdivision is large enough. If the piece of memory is too large, it s subdivided into two pieces. When the piece of memory is freed, the pointer is added to the list of available subdivisions. When two freed subdivisions exist beside each other, they are merged into one piece of free memory. The cost of finding, splitting, and merging memory blocks is expensive and inefficient. .NET does the same thing as C and C++ in that it searches, allocates, and releases memory. Where .NET and other runtimes are different is that they make assumptions on how to search, allocate, and free memory. These assumptions, which are available to C and C++ in the form of runtimes, dramatically increase the performance of an application. The result is that allocating and freeing objects using a runtime costs less time from a CPU perspective than using synchronization. Granted, more resources are required, but these days, with 1GB RAM equipped machines, it s feasible to make those assumptions. As smart as the new generation of memory managers are, there are limits on what should be repeatedly allocated and freed. For example, if an object is 40MB in size, then it shouldn t be allocated and freed as often as an object that is only 1MB. It s still possible to use large immutable objects via the Object Pool pattern. Following are three different types that illustrate the differences in performance: class Regular { private int _value; public Regular( int initial) { _value = initial; } public Regular Increment() { _value ++; return this; } }
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