![]() ![]() I've reduced the matrix size from N=4000 to N=2000 as my machine won't run the timing function for SumMean with N=4000 (hence my suspicion about mean and resources). I hypothesize that this may be a mean function resource issue. However, there are odd peaks in the mean function times. On my Mathcad 11.2a, Pentium III, 256 MB RAM, NT 4.0 PC, I note that Tom's mean function is generally faster than your nice one. On this basis, I've modified your worksheet a little bit and added a timing function to allow slightly more consistent comparative timings. I have a (qualitative) suspicion that Mathcad 'gives up' less time to other Weirdows applications when an algorithm is implemented as a program. Note that both solutions are considerably faster than the original double sum method that I was using (28.5 s on machine #1).Ĭould the significant speed differences that we're seeing here be due to changes in the way things are implemented across different versions?Ĭall me Paranoid, but I'm a bit suspicious of timings that arise from multi-region calculations. So, the SumMatrix program is much faster on one of my machines and somewhat faster on the other. I really expected the second machine to be faster.perhaps extra overhead associated with XP vs 2000? ![]() It never crashes and even with less physical memory, never gives me "out of memory" errors (while the first computer does). This is my workhorse calculator - nothing installed other than Mathcad, Matlab, and some numerical packages. My second machine is a Dell P4 also at 2.4 GHz, but with only 512 MB of RAM. I tend to get lots of Mathcad crashes and "unknown error" messages on this machine. SUM ELEMENTS IN VSTACK ARRAY WINDOWSI'm running Mathcad 2001 under Windows 2000. I have two computers - one is a Gateway P4 at 2.4 GHz with 1 GB of RAM. We'll start out getting comfortable with the basic construction, manipulation and operations of arrays. ![]() To overcome this, we're going to introduce Numpy arrays as a way to get close to compiled level performance while still bring able to use a high-level description. If we wanted to do something like apply a Gaussian blur on a small neighborhood of each pixel using a convolution, we've just increased the number of steps by an order of magnitude!Īnother example is, if we try time-stepping a hyperbolic PDE like an advection equation using a finite difference method using a fine grid, we need to do a potentially large number of operations per step, so we'd like to make this as fast as possible to get any sort of long term evolution. However, as the data sets grow, the overhead of using Python compared to a compiled language like C++ starts to show.įor example, if we wanted to do some image processing for a desktop wallpaper image (say 1280 x 1024 pixels large), just to access every pixel is quite a few steps. In this case, we had a very small and simple data set - roughly 100 differents numbers - so performance wasn't an issue. We left off by introducing a couple tools from Scipy and Matplotlib for basic data processing and visualization. ![]()
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