The LHCb upgrade program implies a significant increase in data processing that will not be matched by additional computing resources. Furthermore, new architectures such as many-core platforms can currently not be fully exploited due to memory and I/O bandwidth limitations. A considerable refactoring effort will therefore be needed to vectorize and parallelize the LHCb software, to minimize hotspots and to reduce the impact of bottlenecks. It is crucial to guide refactoring with a profiling system that gives hints to regions in source-code for possible and necessary re-engineering and which kind of optimization could lead to final success.

Software optimization is a sophisticated process where all parts, compiler, operating system, external libraries and chosen hardware play a role. Intended improvements can have different effects on different platforms. To obtain precise information of the general performance, to make profiles comparable, reproducible and to verify the progress of performance in the framework, it is crucial to produce profiles more systematically in terms of regular profiling based on representative use cases and to perform regression tests. Once a general execution, monitoring and analysis platform is available, software metrics can be derived from the collected profiling results to trace changes in performance back and to create summary reports on a regular basis with an alert system if modifications led to significant performance degradations.