Sophisticated empirical methods drive the development of high-performance solvers for an increasing range of problems from industry and academia. However, automated tools implementing these methods are often difficult to develop and to use. We address this issue with two contributions. First, we develop a formal description of
and use it as the basis for an automated algorithm analysis and design framework called the High-performance Algorithm Laboratory. Second, we describe HAL 1.0, an implementation of the core components of this framework that provides support for distributed execution, remote monitoring, data management, and analysis of results. We demonstrate our approach by using HAL 1.0 to conduct a sequence of increasingly complex analysis and design tasks on state-of-the-art solvers for
and mixed-integer programming problems.