BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//CERN//INDICO//EN
BEGIN:VEVENT
SUMMARY:Python-on-GPUs
DTSTART:20261126T080000Z
DTEND:20261126T120000Z
DTSTAMP:20261030T102400Z
UID:indico-event-568@events.hpc-portal.eu
DESCRIPTION:\nAccelerate Scientific Computing\, Data Science\, and Machin
 e Learning with Python on GPUs\n\n \nThe Python programming language is i
 ncreasingly popular. It is a versatile language for general purpose progra
 mming and accessible for novice programmers. However\, it is also increasi
 ngly used for scientific computing and can be used to develop code that ru
 ns on GPGPUs. Additionally\, a number of libraries that are commonly used 
 in scientific computing\, data science and machine learning can use GPGPUs
  to improve performance. \nDescription \nPython is making inroads into t
 he HPC landscape. However\, writing Python code for efficient scientific 
 computing is not entirely trivial. In this course a variety of technique
 s and libraries will be discussed that are useful in this context. Subjec
 ts covered include profiling of code to discover opportunities for optimi
 zation\, using Cython\, a Python extension that translates critical code 
 sections into efficient C\, wrapping C/C++/Fortran libraries in Python\,
   multithreaded/multiprocess Python\, distributed programming use mpi4py
 \, and pySpark for data science. \nTarget audience  \nThis training is
  for you if you speed up your Python by using GPUs. \nPrevious knowledge
   \nYou will need experience programming in Python. This is not a traini
 ng that starts from scratch. Some familiarity with numpy is required as 
 well.\nIf you plan to do Python GPU programming in a Linux or HPC environm
 ent (and you should)\, then familiarity with these environments is requir
 ed as well. \nResult/Objectives  \nWhen you complete this training yo
 u will\n\nhave an understanding of the architecture and features of GPGPUs
 \,\nbe able to transfer data between the host and the GPGPU device\,\nbe a
 ble to do linear algebra computations on GPGPUs using scikit-cuda\,\nbe ab
 le to generate random numbers on a GPGPU using curand\,\nbe able to define
  your own kernels to run on GPGPUs\,\nuse numba to generate kernels to run
  on GPGPUs\,\nrun machine learning algorithms on GPGPUs\,\nspeed up data s
 cience tasks using Rapids. \n\nInstructor \nGeert Jan Bex\n\nhttps://eve
 nts.hpc-portal.eu/event/568/
URL:https://events.hpc-portal.eu/event/568/
END:VEVENT
END:VCALENDAR