In high-performance computing, performance analysis, tuning, and exploration are relevant throughout the life cycle of an application. State-of-the-art tools provide capable measurement infrastructure, but they lack automation of repetitive tasks, such as iterative measurement-overhead reduction, or tool support for challenging and time-consuming tasks, e.g., mini-app creation. In this thesis, we address this situation with (a) a comparative study on overheads introduced by different tools, (b) the tool PIRA for automatic instrumentation refinement, and (c) a tool-supported approach for mini-app extraction. In particular, we present PIRA for automatic iterative performance measurement refinement. It performs whole-program analysis using both source-code and runtime information to heuristically determine where in the target application measurement hooks should be placed for a low-overhead assessment. At the moment, PIRA offers a runtime heuristic to identify compute-intensive parts, a performance-model heuristic to identify scalability limitations, and a load imbalance detection heuristic. In our experiments, PIRA compared to Score-P’s built-in filtering significantly reduces the runtime overhead in 13 out of 15 benchmark cases and typically introduces a slowdown of < 10 %. To provide PIRA with the required infrastructure, we develop MetaCG — an extendable lightweight whole-program call-graph library for C/C++. The library offers a compiler-agnostic call-graph (CG) representation, a Clang-based tool to construct a target’s CG, and a tool to validate the structure of the MetaCG. In addition to its use in PIRA, we show that whole-program CG analysis reduces the number of allocation to track by the memory tracking sanitizer TypeART by up to a factor of 2,350×. Finally, we combine the presented tools and develop a tool-supported approach to (a) identify, and (b) extract relevant application regions into representative mini-apps. Therefore, we present a novel Clang-based source-to-source translator and a type-safe checkpoint-restart (CPR) interface as a common interface to existing MPI-parallel CPR libraries. We evaluate the approach by extracting a mini-app of only 1,100 lines of code from an 8.5 million lines of code application. The mini-app is subsequently analyzed, and maintains the significant characteristics of the original application’s behavior. It is then used for tool-supported parallelization, which led to a speed-up of 35 %. The software presented in this thesis is available at https://github.com/tudasc.