DescriptionThis tutorial presents state-of-the-art performance tools for leading-edge HPC systems founded on the community-developed Score-P instrumentation and measurement infrastructure, demonstrating how they can be used for performance engineering of effective scientific applications based on standard MPI, OpenMP, hybrid combination of both, and increasingly common usage of accelerators. Parallel performance tools from the Virtual Institute – High Productivity Supercomputing (VI-HPS) are introduced and featured in hands-on exercises with Score-P, Scalasca, Vampir, and TAU. We present the complete workflow of performance engineering, including instrumentation, measurement (profiling and tracing, timing and PAPI hardware counters), data storage, analysis, tuning, and visualization. Emphasis is placed on how tools are used in combination for identifying performance problems and investigating optimization alternatives. Using their own notebook computers, participants will conduct exercises on a contemporary HPC system where remote access will be provided for the hands-on sessions through AWS running an E4S [http://e4s.io] image containing all of the necessary tools. This image supports NVIDIA GPUs using CUDA 12 and Python. This will help to prepare participants to locate and diagnose performance bottlenecks in their own parallel programs.