Auto-tuning is a popular approach to program optimization: it automatically finds good configurations of a program’s so-called tuning parameters whose values are crucial for achieving high performance for a particular parallel architecture and characteristics of input/output data. We present three new contributions of the Auto-Tuning Framework (ATF), which enable a key advantage in general-purpose auto-tuning : efficiently optimizing programs whose tuning parameters have interdependencies among them. We make the following contributions to the three main phases of general-purpose auto-tuning: (1) ATF generates the search space of interdependent tuning parameters with high performance by efficiently exploiting parameter constraints; (2) ATF stores such search spaces efficiently in memory, based on a novel chain-of-trees search space structure; (3) ATF explores these search spaces faster, by employing a multi-dimensional search strategy on its chain-of-trees search space representation. Our experiments demonstrate that, compared to the state-of-the-art, general-purpose auto-tuning frameworks, ATF substantially improves generating, storing, and exploring the search space of interdependent tuning parameters, thereby enabling an efficient overall auto-tuning process for important applications from popular domains, including stencil computations, linear algebra routines, quantum chemistry computations, and data mining algorithms.

We investigate the formal verification of the control software of critical systems, i.e., the verification of the compliance of the designed system with th