On the Landscape of Scientific Computing Libraries in Python

Python has seen large-scale adoption as a highly productive language for scientific computing, primarily due to its rich ecosystem of libraries such as NumPy, PyTorch, and TensorFlow. These libraries claim to deliver scalable and portable performance without the low-level complexities associated with traditional high-performance compiled languages such as C, C++, and Fortran. However, they are predominantly designed and optimized for machine learning workloads.This work quantifies and characterizes the performance, productivity, and memory efficiency of these libraries for using four scientific computing workloads from the field of computational physics. In addition, we examine the influence of features offered by these libraries, including auto-parallelization, vectorization, and just-in-time (JIT) compilation. Using NumPy as the baseline and C++ as the performance upper bound, we analyze the compute and memory characteristics of each library, along with the associated runtime overhead within the Python ecosystem for solving these problems. Such a characterization enables an accurate quantification of the productivity-performance tradeoffs among the different libraries, both relative to each other and to C++. Finally, we leverage these insights to propose guidelines that can assist programmers and scientists in selecting the most suitable libraries for their work.