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PythonHPC

License: BSD 3-Clause "New" or "Revised" License

Python 4.08% Jupyter Notebook 95.16% Makefile 0.06% C 0.13% Fortran 0.55% Shell 0.03%

pythonhpc's Introduction

This repository contains the material used for the `High-Performance Computing with Python` course, organized at CSCS on 11-13 November 2019.

The course covers the following topics:

Interactive parallel programming with IPython
Profiling and optimization
High-performance NumPy
Just-in-time compilation with Numba
Distributed-memory parallel programming with Python and MPI
Bindings to other programming languages and HPC libraries
Interfaces to GPUs

Course Instructors:

Dr. Rafael Sarmiento (Computational Scientist, CSCS)
Dr. Tim Robinson (Computational Scientist, CSCS)
Dr. Theofilos Manitaras (Computational Scientist, CSCS)
Dr. Vasileios Karakasis (Group Lead, CSCS)

Agenda

Monday, November 11, 2019

(10:00 - 10:45) Introduction to HPC and Python for HPC
(10:45 - 12:00) Python distributions and tools for HPC
(12:00 - 13:00) Lunch
(13:00 - 14:45) Vectorization: NumPy/SciPy stack
(14:45 - 15:00) Coffee break
(15:00 - 15:45) Profiling and memory management in Python
(15:45 - 17:00) Just in time compilation with Numba (1)

Tuesday, November 12, 2019

(09:00 - 09:30) Just in time compilation with Numba (2)
(09:30 - 10:15) Dask: task graphs
(10:15 - 10:30) Coffee break
(10:30 - 11:40) Dask: data structures; distributed
(11:40 - 12:00) Visit to the Machine Room
(12:00 - 13:00) Lunch
(13:00 - 14:00) Language bindings: F2PY
(14:00 - 14:45) Language bindings: CFFI (1)
(14:45 - 15:00) Coffee break
(15:00 - 15:30) Language bindings: CFFI (2)
(15:30 - 16:00) Language bindings: other solutions
(16:00 - 17:00) Optimizing code with Cython (1)

Wednesday, November 13, 2019

(09:00 - 10:15) Optimizing code with Cython (2)
(10:15 - 10:30) Coffee break
(10:30 - 11:00) Fast evaluation of array expressions with NumExpr
(11:00 - 12:00) Just in time compilation with Numba: GPU (1)
(12:00 - 13:00) Lunch
(13:00 - 13:30) Just in time compilation with Numba: GPU (2)
(13:30 - 14:45) Computing on the GPU with CuPy
(14:45 - 15:00) Coffee break
(15:00 - 15:45) Parallelizing workflows with IPyParallel and MPI4Py
(15:45 - 16:00) Conclusions

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