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Matlab vectorize4/19/2023 What we are going to use here are macros. It is chalk-full of metaprogramming abilities. This is where Julia being a newer langugage comes to the rescue. Thus in MATLAB’s inability to metaprogram means you have to write a lot of code. …), then you will have at least a 2x slowdown in your code over written the function yourself. It also causes problems since MATLAB’s anonymous functions are notoriously slow and so if you write a function that makes a function and you make it into an anonymous function (i.e. MATLAB’s inability to metaprogram means we needed to go to C to write a loop, but that loop only works for exactly the type of inputs we had. If you aren’t familiar with metaprogramming, it is simply using programs to write programs. However, we still have the problem we noted with MATLAB that the most efficient way would be to de-vectorize and write a loop which does multiple operations at once. With that packages you can plug it in and call the functions with ease (once vdmul gets added…). * we have to ask, can we do better? Well, the first things we can do is use MKL VML bindings in Julia. So moral of the story, A.*B uses broadcast which will beat your simple loop because of cache-control. Which shows that it speeds up the operation by storing the function in cache. * (x:: Real, r::OrdinalRange ( )įunc = get ! cache_f_na nd $gbf ($gbb, nd, narrays, f ) * ) # 26 methods for generic function ".*": However, this STILL isn’t optimal! Too see this, let us write out what MATLAB’s interpreter turns this into: Fun! Number of Operations ~ Number of Calls Thus to do this in MATLAB you have to write some C code. MATLAB has a page on how to use the max interface to call BLAS/LINPACK functions and using this with the v?mul (i.e. * with vector operations from MKL by directly calling MKL VML functions. How can we do better? Well, we can replace. So the simple A.*B is good for prototyping, but from benchmarking many SPDE solvers I realized this was holding me back. This is huge since processor technologies like AVX2 allows specific vector operations to do calls on 8 numbers at a time, making highly-parallel math operations like this get close to an 8x speedup if used correctly (and AVX3 is coming out soon to double that!). Another thing is that, as far as I can tell, A.*B does not have all of the processor-specific optimizations to make this operation super fast. Therefore A*B gets accelerated whereas A.*B does not. First of all, if you are using co-processors/accelerators such as the Xeon Phi, the option of automatically offloading highly-vectorized problems to a GPU-like device only offloads BLAS/LINPACK calls. these functions will run parallel on your multi-core machine), and much more!īut A.*B doesn’t do a BLAS call! When you use A.*B in MATLAB, it uses its own C-code to loop through an perform this operation. My question to you is why this question keeps getting asked.So every time you call svd, A*B, etc., you are actually calling a highly-optimized state of the art C/Fortran/Assembly code mixture which does all sorts of things to make sure you don’t have cache misses, have the function multi-threaded (i.e. With struct arrays with fields that are more suitable for a cell array using curly braces, in place of the square brackets.Ībove, the user replaced this with plot() Using the square brackets,, just places that list into between the brackets, creating an array of one type, in this case, doubles. How do I collect multiple values into an array? I can use either How do I know this? Because, if I leave the semi-colon off, I see a bunch of output with ans = in other words, I am getting multiple output values. This is because states.x produces a comma-separated list. With an array of structs, you can gather the x values using Let's see what's in states.x states.x ans = Let's create an array of structures first. Access all of the entries of one element as vector. Access individual structsĮnd plot(state.x) % 2. Warrant the use of a simple 2 x N matrix. In my case 'state' is reasonably complicated and hence does not Possible) is: state = an array of structs, say N items each with (for example)Ĭomponents x and y. In a nutshell, what I am trying to do (in as few lines of code as Here is one customer's description of his problem: And also got an email from a customer on this topic. Recently there was a question on the newsgroup about how to vectorize the access to elements in a structure array.
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