  The Julia programming language has evolved into a modern alternative to fill
existing gaps in scientific computing and data science applications. Julia
leverages a unified and coordinated single-language and ecosystem paradigm and
has a proven track record of achieving high performance without sacrificing
user productivity. These aspects make Julia a viable alternative to
high-performance computing's (HPC's) existing and increasingly costly many-body
workflow composition strategy in which traditional HPC languages (e.g.,
Fortran, C, C++) are used for simulations, and higher-level languages (e.g.,
Python, R, MATLAB) are used for data analysis and interactive computing.
Julia's rapid growth in language capabilities, package ecosystem, and community
make it a promising universal language for HPC. This paper presents the views
of a multidisciplinary group of researchers from academia, government, and
industry that advocate for an HPC software development paradigm that emphasizes
developer productivity, workflow portability, and low barriers for entry. We
believe that the Julia programming language, its ecosystem, and its community
provide modern and powerful capabilities that enable this group's objectives.
Crucially, we believe that Julia can provide a feasible and less costly
approach to programming scientific applications and workflows that target HPC
facilities. In this work, we examine the current practice and role of Julia as
a common, end-to-end programming model to address major challenges in
scientific reproducibility, data-driven AI/machine learning, co-design and
workflows, scalability and performance portability in heterogeneous computing,
network communication, data management, and community education. As a result,
the diversification of current investments to fulfill the needs of the upcoming
decade is crucial as more supercomputing centers prepare for the exascale era.
