Publications

A broad set of data science and engineering questions may be organized as graphs, providing a powerful means for describing relational data. Although experts now routinely compute graph algorithms on huge, unstructured graphs using high performance computing (HPC) or cloud resources, this practice hasn't yet broken into the mainstream. Such computations require great expertise, yet users often need rapid prototyping and development to quickly customize existing code. Toward that end, we are exploring the use of the Chapel programming language as a means of making some important graph analytics more accessible, examining the breadth of characteristics that would make for a productive programming environment, one that is expressive, performant, portable, and robust.