As the computing needs of the world have grown, the need for parallel systems has grown to match. However, the programming languages used to target those systems have not had the same growth. General parallel programming targeting distributed CPUs and GPUs is frequently locked behind low-level and unfriendly programming languages and frameworks. Programmers must choose between parallel performance with low-level programming or productivity with high-level languages.

Chapel is a programming language for productive parallel programming that scales from laptops to supercomputers. This talk will focus on the ways that Chapel addresses the above gap, giving programmers used to high level languages like Python access to distributed parallel performance. Chapel has long been open-source, but recently moved to become one of the many amazing projects hosted under the High Performance Software Foundation.

The talk will include a description of Chapel and its performance as well as a few examples of Chapel programs. I will also present Arkouda, an exploratory data science tool for massive scales of data. Arkouda is built in Chapel and completely closes the accessibly gap for Python programmers to access supercomputer-scale data analysis.