Fundamentals of Performance Tuning for Python Applications, 3/22/17
Python is a dynamic, general-purpose programming language that is widely used in all fields of scientific computing. The Python language is easy to learn, equipped with a great standard library, and it’s the center of a number of third-party libraries. With Python one can build everything from simple scripts to complex applications, with fewer lines of code than one could think possible.
The course will discuss fundamentals of performance tuning for Python applications, providing ways to make Python applications run faster. This will include learning how to use tools like Numpy, Cython, Tensorflow, Numba as well as techniques like using generators and sorting keys, interfacing Python with compiled code with Ctypes and SWIG, parallelization with Multiprocessing and Joblib libraries.
Alexey Svyatkovskiy is a Big Data, Software and Programming Analyst with the Princeton Institute for Computational Science & Engineering (PICSciE). He holds a PhD in particle physics and has over 5 years of experience in large scale data analysis and machine learning. His work has been presented at IEEE Big Data conference and Spark Summit.
Please register online at the training website, www.princeton.edu/training or contact Andrea Rubinstein at firstname.lastname@example.org /258-1397.
Location: Room 347, Visualization Lab
Date/Time: 03/22/17 at 10:00 am - 03/22/17 at 4:00 pm