Practical Design Space Exploration
Luigi Nardi, David Koeplinger, Kunle Olukotun

TL;DR
This paper introduces HyperMapper 2.0, a novel, interpretable framework for multi-objective design space exploration that effectively handles unknown constraints and categorical variables, improving optimization efficiency in hardware tuning.
Contribution
The paper presents HyperMapper 2.0, a new methodology and software that advances design space exploration by supporting unknown constraints, categorical variables, and user priors, with an interpretable model.
Findings
HyperMapper 2.0 outperforms state-of-the-art baselines in Pareto front quality.
It achieves up to 8x reduction in sampling budget.
The framework provides better hypervolume indicators across benchmarks.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
