In this session you will learn different ways to optimize and accelerate your MATLAB code to reduce the execution time of computationally intensive MATLAB applications. We will address common pitfalls in writing MATLAB code and explore the use of the MATLAB Profiler to find bottlenecks. We will also introduce high-level programming constructs from the Parallel Computing Toolbox that allow you to create and run parallel MATLAB applications on multicore processors, GPU and clusters without low-level CUDA or MPI programming.
Highlights include:
‧Optimizing MATLAB code to boost execution speed
‧Creating parallel applications to speed up independent tasks
‧Scaling up to computer clusters, grid environments or clouds
‧Employing GPUs to speed up your computations
‧Converting MATLAB Code to C for faster execution