In the old days of computing, life was relatively simple as far as processor speed was concerned.\u00a0 If your current computer ran at 1Ghz and you were about to buy one that ran at 2Ghz then, as a rough rule of thumb, you expected it to be about twice as fast.\u00a0 Of course there was more to it than that (there always is) and other things such as amount of memory, hard disk speed etc etc also had an impact on how fast you got the results of your calculations.\u00a0 It also didn’t help that not all Ghz were created equally – two chips that ran at the same clock speed but with different architectures could exhibit radically different calculation performance.<\/p>\n
I did say life was ‘relatively simple’ not just ‘simple’.<\/p>\n
These days we still have all of the above to worry about, but we also have the added complication that a typical modern computer contains not one, but two processors.\u00a0 It’s almost like having two machines for the price of one and higher end desktops can contain as many as 8.\u00a0 Which is a lot!<\/p>\n
Back in the old, single processor, days we could take our slow code from an old machine, put it on a new machine and it would go faster.\u00a0\u00a0 That was it – no worries, no hassle.\u00a0 If only life were so simple today….<\/p>\n
These days we need to actually rewrite our code so that it makes better use of all the idle processors lying around in our new fangled machines or, to put it more technically, we need to parallelize our code.\u00a0 Sometimes this is simple, often it’s not but one thing is for sure and that is that the environment we are programming in has to provide a set of parallel programming commands to help us do this extra work.<\/p>\n
Old style languages such as C and FORTRAN have had this sort of stuff for years in the form of libraries such as Open MPI<\/a> and Open MP<\/a> but mathematical programming languages such as Mathematica and Maple have only recently gotten in on the act.<\/p>\n Version 7<\/a> of Mathematica and Version 13 of Maple*<\/a> both included a set of tools for enabling parallel programming as part of their basic installs.\u00a0 In other words – you don’t need to pay any extra money to get these programs to use your multi-processor computers to their full potential.<\/p>\n MATLAB, on the other hand, expects you to pay extra to be able to parallelize\u00a0 your code by requiring you to purchase the Parallel Computing Toolbox<\/a>.\u00a0 It’s not cheap either!\u00a0 Of course, Mathworks are free to charge whatever they like for their products but I don’t think that this particular policy is doing them any favours.<\/p>\n