Comparing Python Code Parallelization Techniques for Spatial Transcriptomics Data

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 resources available by means of parallelization. In this paper, we compare several Python-based parallelization frameworks and libraries and evaluate them using a simple processing algorithm on spatial transcriptomics data. The results show speedups of up to 60x with significant variation in the execution time depending on the framework used. The results and experiences are discussed in the paper.