  With current semiconductor technology reaching its physical limits,
special-purpose hardware has emerged as an option to tackle specific
computing-intensive challenges. Optimization in the form of solving Quadratic
Unconstrained Binary Optimization (QUBO) problems, or equivalently Ising spin
glasses, has been the focus of several new dedicated hardware platforms. These
platforms come in many different flavors, from highly-efficient hardware
implementations on digital-logic of established algorithms to proposals of
analog hardware implementing new algorithms. In this work, we use a mapping of
a specific class of linear equations whose solutions can be found efficiently,
to a hard constraint satisfaction problem (3-regular 3-XORSAT, or an Ising spin
glass) with a 'golf-course' shaped energy landscape, to benchmark several of
these different approaches. We perform a scaling and prefactor analysis of the
performance of Fujitsu's Digital Annealer Unit (DAU), the D-Wave Advantage
quantum annealer, a Virtual MemComputing Machine, Toshiba's Simulated
Bifurcation Machine (SBM), the SATonGPU algorithm from the JANUS collaboration,
and our implementation of parallel tempering. We identify the SATonGPU and DAU
as currently having the smallest scaling exponent for this benchmark, with
SATonGPU having a small scaling advantage and in addition having by far the
smallest prefactor thanks to its use of massive parallelism. Our work provides
an objective assessment and a snapshot of the promise and limitations of
dedicated optimization hardware relative to a particular class of optimization
problems.
