MATLAB Parallel Computing Toolbox - Parallelization vs GPU?

When you use parfor, you are effectively dividing your for loop into tasks, with one task per loop iteration, and splitting up those tasks to be computed in parallel by several workers where each worker can be thought of as a MATLAB session without an interactive GUI. You configure your cluster to run a specified number of workers on each node of the cluster (generally, you would choose to run a number of workers equal to the number of available processor cores on that node).

On the other hand, gpuarray indicates to MATLAB that you want to make a matrix available for processing by the GPU. Underneath the hood, MATLAB is marshalling the data from main memory to the graphics board's internal memory. Certain MATLAB functions (there's a list of them in the documentation) can operate on gpuarrays and the computation happens on the GPU.

The key differences between the two techniques are that parfor computations happen on the CPUs of nodes of the cluster with direct access to main memory. CPU cores typically have a high clock rate, but there are typically fewer of them in a CPU cluster than there are GPU cores. Individually, GPU cores are slower than a typical CPU core and their use requires that data be transferred from main memory to video memory and back again, but there are many more of them in a cluster. As far as I know, hybrid approaches are supposed to be possible, in which you have a cluster of PCs and each PC has one or more Nvidia Tesla boards and you use both parfor loops and gpuarrays. However, I haven't had occasion to try this yet.

If you are mainly interested in simulations, GPU processing is the perfect choice. However, if you want to analyse (big) data, go with Parallization. The reason for this is, that GPU processing is only faster than cpu processing if you don't have to copy data back and forth. In case of a simulation, you can generate most of the data on the GPU and only need to copy the result back. If you try to work with bigger data on the GPU you will very often run into out of memory problems. Parallization is great if you have big data structures and more than 2 cores in your computer CPU.