Abstract
As computers scale up dramatically to tens and hundreds of thousands of cores, develop deeper
computational and memory hierarchies, and increased heterogeneity, developers of scientific software are
increasingly challenged to express complex parallel simulations effectively and efficiently. In this paper, we
explore the three languages developed under the DARPA High-Productivity Computing Systems (HPCS)
program to help address these concerns: Chapel, Fortress, and X10. These languages provide a variety
of features not found in currently popular HPC programming environments and make it easier to express
powerful computational constructs, leading to new ways of thinking about parallel programming. Though
the languages and their implementations are not yet mature enough for a comprehensive evaluation, we
discuss some of the important features, and provide examples of how they can be used in scientific
computing. We believe that these characteristics will be important to the future of high-performance
scientific computing, whether the ultimate language of choice is one of the HPCS languages or something
else.
