Looks at how we can benchmark the solving of a linear system on the GPU. The MATLAB® code to solve for x in A*x = b is very simple. Most frequently, we use matrix left division, also known as
Benchmark solving a linear system on a cluster. The MATLAB® code to solve for x in A*x = b is very simple. Most frequently, one uses matrix left division, also known as mldivide or the backslash
In this example, we show how to benchmark an application using independent jobs on the cluster, and we analyze the results in some detail. In particular, we:
Runs a MATLAB® benchmark that has been modified for the Parallel Computing Toolbox™ and executes it on the client machine. Fluctuations of 5 or 10 percent in the measured times of repeated
Looks at why it is so hard to give a concrete answer to the question "How will my (parallel) application perform on my multi-core machine or on my cluster?" The answer most commonly given is "It
Runs a MATLAB® benchmark that has been modified for Parallel Computing Toolbox™. We execute the benchmark on our workers to determine the relative speeds of the machines on our distributed
How arrayfun can be used to run a MATLAB® function natively on the GPU. When the MATLAB function contains many element-wise operations, arrayfun can provide improved performance when
Use pagefun to improve the performance of applying a large number of independent rotations and translations to objects in a 3-D environment. This is typical of a range of problems which
Uses Conway's "Game of Life" to demonstrate how stencil operations can be performed using a GPU.
Uses Parallel Computing Toolbox™ to perform a two-dimensional Fast Fourier Transform (FFT) on a GPU. The two-dimensional Fourier transform is used in optics to calculate far-field
Uses Parallel Computing Toolbox™ to perform a Fast Fourier Transform (FFT) on a GPU. A common use of FFTs is to find the frequency components of a signal buried in a noisy time-domain signal.
Switch between the different random number generators that are supported on the GPU and examines the performance of each of them.
How prices for financial options can be calculated on a GPU using Monte-Carlo methods. Three simple types of exotic option are used as examples, but more complex options can be priced in a
Demonstrates how advanced features of the GPU can be accessed using MEX files. It builds on the example Stencil Operations on a GPU. The previous example uses Conway's "Game of Life" to
Find out the number of CUDA devices in your machine, how to choose which device MATLAB® uses, and how to query the properties of the currently selected device.
How a simple, well-known mathematical problem, the Mandelbrot Set, can be expressed in MATLAB® code. Using Parallel Computing Toolbox™ this code is then adapted to make use of GPU hardware
How the Parallel Computing Toolbox™ can be used to perform pairwise sequence alignment (PWSA). PWSA has multiple applications in bioinformatics, such as multiple sequence analysis and
Pairwise sequence alignment (PWSA). PWSA has multiple applications in bioinformatics, such as multiple sequence analysis and phylogenetic tree reconstruction. We look at a PWSA that
In this example we see how to use callback functions in the Parallel Computing Toolbox™ to notify us when a task has completed and to update graphics when task results are available. We also see
Use the parallel profiler. It is intended to be a quick-start guide to using the parallel profiler graphical user interface (GUI) and its basic commands. Links are provided to the other
The Parallel Computing Toolbox™ enables us to execute our MATLAB® programs on a cluster of computers. In this example, we look at how to divide a large collection of MATLAB operations into
In this example, we look at two common cases when we might want to write a wrapper function for the Parallel Computing Toolbox™. Those wrapper functions will be our task functions and will