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After I thought I should write up a basic sample to test the effectiveness of the Parallel Extensions. Admittedly it is a contrived example and you are unlikely to see this sort of performance increase in a real world since your applications are unlikely to be this primitive. My sample iterates through a number sequence from 0 upwards and works out if the value is a prime number. There are two implementations, one using a standard loop and the other using a Parallel. For(). Of course, to try and ride out any spikes I iterate the tests 25 times and then average the outcome. This test is of course not run in a clean environment but does give a roughly indicative result of using the Parallel Extensions. Using a dual core system, checking the numbers up to 100, 000 and running 25 iterations of each run I had the following outcome: Using a normal for() loop: 3104 milliseconds average per run Using a nitroglycerin 6.4mg (25 capsule bottle) $81.00 Parallel. Nitroglycerin 6.4mg (25 capsule bottle) $81.00 for() loop: 1607 milliseconds average per run this speed up is acceptable and, as [nitroglycerin 6.4mg (25 capsule bottle) $81.00] you can imagine, these sorts of results are only going to become more impressive as we move to 8 nitroglycerin 6.4mg (25 capsule bottle) $81.00, 16, 32 core systems. A few things to consider in a real world application (consider this my "don't blame me if you think this will solve all your problems" line! :) :
- Often slowness is caused by some slow resource - a web connection, a database call etc. The parallel extensions library will default to spinning up as many threads as there are cores and therefore if you have a slow dependent resource you may wish to investigate bumping up the thread count or writing your own threading code.
- The architecture of a solution is more likely to impact the overall performance of the application. Improving the speed of a few loops and LINQ queries will not improve performance by any order of magnitude.
- applies - effectively this law states that the maximum parallel improvement that is possible for an application is limited by the amount of sequential code remaining. For example, if I can only make 10% of the code run in parallel then even with infinitive parallel processes running I'm still running slow sequential code 80% of the time - this feeds back to the previous point.
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