  We present POLO --- a C++ library for large-scale parallel optimization
research that emphasizes ease-of-use, flexibility and efficiency in algorithm
design. It uses multiple inheritance and template programming to decompose
algorithms into essential policies and facilitate code reuse. With its clear
separation between algorithm and execution policies, it provides researchers
with a simple and powerful platform for prototyping ideas, evaluating them on
different parallel computing architectures and hardware platforms, and
generating compact and efficient production code. A C-API is included for
customization and data loading in high-level languages. POLO enables users to
move seamlessly from serial to multi-threaded shared-memory and multi-node
distributed-memory executors. We demonstrate how POLO allows users to implement
state-of-the-art asynchronous parallel optimization algorithms in just a few
lines of code and report experiment results from shared and distributed-memory
computing architectures. We provide both POLO and POLO.jl, a wrapper around
POLO written in the Julia language, at https://github.com/pologrp under the
permissive MIT license.
