Categories of parallel systems. Parallelization of code in shared and distributed memory systems.
The OpenMP and MPI parallel programming models.
Management of large volumes of data with parallel I/O techniques.
Code optimization techniques and exploitation of performance metrics.
Basic features of graphics accelerators and their exploitation with the CUDA and OpenACC parallel programming models.
Exploitation of parallelism in applications with an emphasis on stochastic optimization algorithms, machine learning problems, and large-scale neural network training.
Exploitation of task parallelism in the Python programming language.
Techniques for efficient data processing and parameter optimization in machine learning.
We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. By clicking “Accept”, you consent to the use of ALL the cookies.
This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience.
