In this webinar, we will focus on GPU-accelerated computing with Python, one of the most popular programming languages for science, engineering, data analytics, and deep learning applications. The basics of Python programming and libraries including Numpy, Pandas, and Matplotlib will be briefly discussed. Representative libraries for GPU programming including CuPy, cuDF, PyCUDA, Jax and Numba will be presented for their specific features for programming accelerations.
The GPU programming using Python webinar is for software developers who want to start using GPUs to accelerate their workflows.
Graphics Processing Units (GPUs), in the past decade, have kickstarted the vibrant field of data science. Did you know that GPUs can do a lot more than machine learning? These devices can accelerate and massively parallelise any general-purpose computational load within big data and number crunching. You can either use the GPU in your personal computer or scale up your application to supercomputers. How does one get started?
However, multiple vendors compete in the high-end GPU market, each vendor provides their own software stack and development toolkits. Moreover, there is a proliferation of tools, languages, and frameworks that one has to take into account when writing code for GPUs.
Therefore, it can be difficult for the developer to choose the right problem size, the right level of abstraction, and the right framework while being compatible with the hardware at hand.
https://enccs.se/events/gpu-programming-using-python-a-practical-intro/
For any questions contact us at training@enccs.se
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This training is intended for users established in the European Union or a country associated with Horizon 2020. You can read more about the countries associated with Horizon2020 here https://ec.europa.eu/info/research-and-innovation/statistics/framework-programme-facts-and-figures/horizon-2020-country-profiles_e
