# Global Benchmark Database

arXiv: https://arxiv.org/abs/2405.10045 · HTML: https://mlsanity.com/paper/2405.10045/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 27 references that are in the map.
- First posted on arXiv 16 May 2024.

## Builds on (most cited first, 2 of 2)

- [ASlib: A Benchmark Library for Algorithm Selection](https://mlsanity.com/paper/1506.02465/) (2015)
- [Effective Auxiliary Variables via Structured Reencoding](https://mlsanity.com/paper/2307.01904/) (2023)

## Built on by (most cited first, 2 of 2)

- [Boolean Satisfiability via Imitation Learning](https://mlsanity.com/paper/2509.25411/) (2025)
- [Learning How to Cube](https://mlsanity.com/paper/2605.16632/) (2026)

## Abstract (arXiv)

This paper presents Global Benchmark Database (GBD), a comprehensive suite of tools for provisioning and sustainably maintaining benchmark instances and their metadata. The availability of benchmark metadata is essential for many tasks in empirical research, e.g., for the data-driven compilation of benchmarks, the domain-specific analysis of runtime experiments, or the instance-specific selection of solvers. In this paper, we introduce the data model of GBD as well as its interfaces and provide examples of how to interact with them. We also demonstrate the integration of custom data sources and explain how to extend GBD with additional problem domains, instance formats and feature extractors.