One of the slowest blocks of code that inflate runtimes in MATLAB are for/while loops. In this blog post, I’m going to talk about a little known way of crushing MATLAB loop runtimes for many commonplace use cases by utilizing one of the most amazingly underrated and unknown functions in MATLAB’s repertoire: bsxfun. Using this function, one can break seemingly iterative code into clean, vectorized, snippets that beat the socks off even MATLAB’s JIT engine. Better still, Jacket fully supports bsxfun meaning that if you thought a vectorized loop was fast, you haven’t seen anything, yet. Also, in the end, a loop represented using bsxfun is just good programming practice. As we’ll see, the technique I’m going to describe is …
Using Parallel For Loops (parfor) with MATLAB® and Jacket
MATLAB® parallel for loops (parfor) allow the body of a for loop to be executed across multiple workers simultaneously, but with some pretty large restrictions. With Jacket MGL, Jacket can be used within parfor loops, with the same restrictions. However, it is important to note that Jacket MGL does not currently support co-distributed arrays. Problem Size Problem size might be the single most important consideration in parallelization using the Parallel Computing Toolbox (PCT) and Jacket MGL. When data is used by a worker in the MATLAB pool it must be copied from MATLAB to the worker, and must be copied back when the computation is complete. Additionally, when GPU data is used, it must then be copied by the worker …