Abstract: Processing of large quantitities of data from bioinformatics domain pose significant challenge for contemporary computing systems. For that reason, it is important to employ all hardware ...

This project includes simple scripts to show how to write and read data from a PostgreSQL database using Python and pandas. The database is assumed to run in a local container (e.g., Docker).

Parallelization is a powerful technique to speed up computations by distributing tasks across multiple CPU cores or threads. This project evaluates the performance of four popular Python ...

Python is convenient because it is easy to program and has a rich library, but its only weak point is that execution is very slow outside of compiled libraries like Numpy. So, I will measure the ...

Parallelization in Python integrates Message Passing Interface via the mpi4py module. Since mpi4py does not support parallelization of objects greater than 2 31 bytes, we developed BigMPI4py, a Python ...