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Both produce the same result: the Count field contains 400,000,000 at the end. But the multithreaded version was much slower. One reason is the difference in how the two versions increment the Count. Here s the line in question from the single-threaded code:

Count += 1;

Figure 7-3. JavaScript HTML order page In the preceding example, the client browser computes the price to be paid based on the number of pizzas the user would like to order. However, as you learned before, you cannot trust the client! A malicious user could simply save the HTML page to disk (as we illustrated earlier in this chapter), delete the JavaScript from the HTML page, substitute 10000 for the quantity and 0 for the price, and submit the form. Alternatively, a malicious user could also just submit an HTTP request such as GET /submit_order qty=1000&price=0&Order=Pay and completely bypass the price computation done by the JavaScript! The solution to eliminating the problem of not being able to trust the client, in this case, is to do the price computation on the server, and charge the user the price that is computed by the server. While JavaScript can be used to make the web page more interactive for the client, any data validation or computations done by the JavaScript cannot be trusted by the server. The computations must be redone on the server to ensure security.

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After the dot com bomb in 2001, a small wave of companies emerged with a different perspective on how to leverage the Internet That leverage came from opening up the customer data their products and services controlled Using web standards that were common and widely adopted across traditional competitors, a new value proposition was created This value proposition supercharged start-ups like Google, Amazon, and eBay and revitalized established Internet players like Microsoft and Yahoo Portals in Web 20 put customers in charge of their data Let s return to the example of portals and now move forward to 2008: myYahoo, Microsoft Live, and iGoogle not only enable the customer to completely control what data they see and what source it comes from, but where on the page and how often it is displayed.

But if we try that in the multithreaded version, it doesn t work. It certainly makes it run nice and quickly about three times faster than the single-threaded version but trying it a few times, Count comes to 110,460,151, then 133,533,503, then 133,888,803... The majority of increments are getting lost that s the sort of thing that happens when you don t use suitable protection for accessing shared state. That s why the multithreaded version needs to do this:

Interlocked.Increment(ref Count);

The Interlocked class in the System.Threading namespace provides methods that perform certain simple operations in ways that work even if multiple threads try to use them on the same data at the same time. As the name suggests, Increment increments the count, and it does this in a way that locks out any other logical processors attempting to do the same thing at the same time. It forces the logical processors to take it in turns. It works the Count total is correct with this code in place but at some cost. On a quad-core system, with all four cores burning away at 100 percent, this takes 15 times longer than the simple single-threaded solution. In fact, the cost of Interlocked.Increment does not fully explain the difference. Modifying the single-threaded version to work the same way makes it run about five times slower, but that s still three times faster than the multithreaded code. So a considerable amount of the slowdown is down to the communication costs between the processors.

Don t take these numbers too seriously. This is clearly a contrived example. (If we wanted it to run really fast we could just have initialized Count to 400,000,000 to start with, and removed the loop.) But while the details are spurious the basic principle applies broadly: the cost of contention between logical processors that are supposed to be cooperating can work against you. Sometimes they merely erode the benefit you might see a 2.5x speedup on a quad-core system, for example. But sometimes they really do negate the benefit contrived though this example may be, much worse examples have cropped up in real systems.

Some implementations may come out worse on some systems and better on others. For example, some parallel algorithms take a considerable hit relative to their sequential counterparts in order to be able to scale well on more processors. Such an algorithm might make sense only on systems where you have a large number of processors it might be slower than the single-threaded version on a dual-core system, but very worthwhile on a system with 16 logical processors, for example.

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