Python is gaining importance in High Performance Computing (HPC) due to the faster and easier development than in more performance-oriented programming languages like C. This makes Python very attractive for prototyping new ideas, but also in performance aspects Python is getting better with modules like NumPy, SciPy and even optimized versions specific for Intel CPUs. Python even has support to access libraries written in C, to utilize the performance of C, while having the object orientation and ease-of-use of Python. But although Python is making progress in High Performance Computing, Python has little direct support for High Performance I/O (HPIO) systems. Since the calculation power in HPC systems is growing much faster than the speed of I/O devices, this is an area which is quickly gaining importance. The goal of this thesis is to evaluate the possibilities of HPIO in Python. Therfore an interface to an existing HPIO system will be implemented using Python's support to access C libraries. The I/O system used for this implementation will be JULEA, which provides a modular design and a object-oriented like implementation in C, which makes the conversion to a real object-oriented language like Python easier. For this implementation, different approaches for the interface between Python and C will be evaluated to ensure a good maintainability and performance.

@misc{PFHPIC19,
	author	 = {Johannes Coym},
	title	 = {{Python for High Performance I/O}},
	advisors	 = {Michael Kuhn},
	year	 = {2019},
	month	 = {07},
	school	 = {Universität Hamburg},
	howpublished	 = {{Online \url{https://wr.informatik.uni-hamburg.de/_media/research:theses:johannes_coym_python_for_high_performance_i_o.pdf}}},
	type	 = {Bachelor's Thesis},
	abstract	 = {Python is gaining importance in High Performance Computing (HPC) due to the faster and easier development than in more performance-oriented programming languages like C. This makes Python very attractive for prototyping new ideas, but also in performance aspects Python is getting better with modules like NumPy, SciPy and even optimized versions specific for Intel CPUs. Python even has support to access libraries written in C, to utilize the performance of C, while having the object orientation and ease-of-use of Python. But although Python is making progress in High Performance Computing, Python has little direct support for High Performance I/O (HPIO) systems. Since the calculation power in HPC systems is growing much faster than the speed of I/O devices, this is an area which is quickly gaining importance. The goal of this thesis is to evaluate the possibilities of HPIO in Python. Therfore an interface to an existing HPIO system will be implemented using Python's support to access C libraries. The I/O system used for this implementation will be JULEA, which provides a modular design and a object-oriented like implementation in C, which makes the conversion to a real object-oriented language like Python easier. For this implementation, different approaches for the interface between Python and C will be evaluated to ensure a good maintainability and performance.},
}