Parallel and GPU Computing Tutorials

Parallel computing is the simultaneous use of multiple compute resources to solve a computational problem:

A problem is broken into discrete parts that can be solved concurrently
Each part is further broken down into a series of instructions
Instructions from each part execute simultaneously on different processors
An overall control/coordination mechanism is employed

Speed Up MATLAB with Multicore Computers

Use parallel for loops (parfor) to run independent iterations in parallel on multi-core CPUs, for problems such as parameter sweeps, optimizations, and Monte Carlo simulations. parfor automates the creation of parallel pools and manages file dependencies so that you can focus on your work. Key functions in several MATLAB and Simulink products have parallel-enabled functions. With Parallel Computing Toolbox, these functions can distribute computations across available parallel computing resources. You can execute parallel applications interactively and in batch.

Accelerate MATLAB with GPUs

Parallel Computing Toolbox enables you to use NVIDIA® GPUs directly from MATLAB using gpuArray. More than 500 MATLAB functions run automatically on NVIDIA GPUs, including fft, element-wise operations, and several linear algebra operations such as lu and mldivide, also known as the backslash operator (\). Key functions in several MATLAB and Simulink products, such as Deep Learning Toolbox, have GPU-enabled functions. You can use GPUs without having to write any additional code, so you can focus on your applications rather than performance tuning. Advanced developers can call their own CUDA code directly from MATLAB. You can utilize multiple GPUs on desktops, compute clusters, and cloud environments.

Process Big Data
Parallel Computing Toolbox extends the tall arrays and MapReduce capabilities built into MATLAB so that you can run on local workers for improved performance. You can then scale tall arrays and MapReduce up to additional resources with MATLAB Parallel Server on traditional clusters or Apache Spark™ and Hadoop® clusters. You can also prototype distributed arrays on the desktop and then scale up to additional resources with MATLAB Parallel Server.


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Parallel and GPU Computing Tutorials