Multi-objective optimization is a crucial matter in computer systems design
space exploration because real-world applications often rely on a trade-off
between several objectives. Derivatives are usually not available or
impractical to compute and the feasibility of an experiment can not always be
determined in advance. These problems are particularly difficult when the
feasible region is relatively small, and it may be prohibitive to even find a
feasible experiment, let alone an optimal one.
We introduce a new methodology and corresponding software framework,
HyperMapper 2.0, which handles multi-objective optimization, unknown
feasibility constraints, and categorical/ordinal variables. This new
methodology also supports injection of the user prior knowledge in the search
when available. All of these features are common requirements in computer
systems but rarely exposed in existing design space exploration systems. The
proposed methodology follows a white-box model which is simple to understand
and interpret (unlike, for example, neural networks) and can be used by the
user to better understand the results of the automatic search.
We apply and evaluate the new methodology to the automatic static tuning of
hardware accelerators within the recently introduced Spatial programming
language, with minimization of design run-time and compute logic under the
constraint of the design fitting in a target field-programmable gate array
chip. Our results show that HyperMapper 2.0 provides better Pareto fronts
compared to state-of-the-art baselines, with better or competitive hypervolume
indicator and with 8x improvement in sampling budget for most of the benchmarks
explored.

HyperMapper 2.0 is a new methodology and software framework that handles multi-objective optimization, unknown feasibility constraints, and categorical/ordinal variables for computer systems design, achieving better Pareto fronts and improved sampling for hardware accelerators.