Portable high-performance Python on CPUs, GPUs, and FPGAs

Python has become the de-facto language for scientific computing. Programming in Python is highly productive, mainly due to its rich science-oriented software ecosystem built around the NumPy module. As a result, the demand for Python support in High Performance Computing (HPC) has skyrocketed. However, the Python language itself does not necessarily offer high performance. In this work, we present a workflow that retains Python’s high productivity while achieving portable performance across different architectures. The workflow’s key features are HPC-oriented language extensions and a set of automatic optimizations powered by a data-centric intermediate representation. We also define a set of around 50 benchmark kernels written in NumPy to evaluate and compare Python frameworks for their portability and performance. We show performance results and scaling across CPU, GPU, FPGA, with 2.47x and 3.75x speedups over previous-best solutions and the first-ever Xilinx and Intel FPGA results of annotated Python.