Efficient development of high performance data analytics in Python (original) (raw)

2020, Future Gener. Comput. Syst.

Our society is generating an increasing amount of data at an unprecedented scale, variety, and speed. This also applies to numerous research areas, such as genomics, high energy physics, and astronomy, for which large-scale data processing has become crucial. However, there is still a gap between the traditional scientific computing ecosystem and big data analytics tools and frameworks. On the one hand, high performance computing (HPC) programming models lack productivity, and do not provide means for processing large amounts of data in a simple manner. On the other hand, existing big data processing tools have performance issues in HPC environments, and are not general-purpose. In this paper, we propose and evaluate PyCOMPSs, a task-based programming model for Python, as an excellent solution for distributed big data processing in HPC infrastructures. Among other useful features, PyCOMPSs offers a highly productive general-purpose programming model, is infrastructure-agnostic, and ...