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    <p>there are two ways to make a parallel version of an algorithm/program.</p> <ol> <li><p>You study the algorithm and write the serial program. Afterwards, you start profiling the program to see where you can obtain speed gains. Those are the places where parallelism might come in handy (might, not will). I call this method the "desparate man's tool". This method is useful (!), but most of the times, the method beneath can provide better performance gains. This way of doing the optimisation method only takes programming and user experience into account.</p></li> <li><p>You take the algorithm and try to figure out an other algorithm that permits parallel handling of the problem. Are there independent calculations or steps in the algorithm, are there parts of the algorithm that can be done before other parts completely finish, ... This could be called "the theoretical approach". Keep in mind that every thread has its overhead, and you don't want the overhead to be bigger than the gain you wish to obtain.</p></li> </ol> <p>In fact, a combination of both is the best way to go (if parallelism is really necessary): first concentrate on method 2 (optimise the algorithm so that is stays scientifically correct, but can be treated in multi threading). Then look at the critical thread (can be found while profiling) and start optimising that thread.</p> <p>As Kerrek SB already told: parallel programming is a very complex topic, with lots of possible pitfalls. And at the end of the road, you should ask yourself: is it worth the effort. After all: loosing weeks of study and programming time to gain some minutes is not worth your while. </p> <p>On the other hand, if your program will run thousands of times, frustrating users due to long waiting times or a lack of responsiveness, than maybe, it could be useful to make a more performant version after all. But again: can't you reach the same goal by optimising a sequential version without the parallel clutter? Lot's of algorithms are of order O(exp(x)) or worse and can be reduced to O(x) or even O(log(x)).</p> <p>Kind regards, PB</p>
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    1. COThank you for explaining this! I will study harder and make all the steps suggested, as this is highly used by biologists.
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