HPC centers run a diverse set of applications from a variety of scientific domains. Every application has different resource requirements, but it is difficult for domain experts to find out what these requirements are and how they impact performance. In particular, the utilization of shared resources such as parallel file systems may influence application performance in significant ways that are not always obvious to the user. We present a tool, REMORA, that is designed to provide the information that is most critical for running efficiently an application in HPC systems. REMORA collects runtime resource utilization data for a particular job execution and presents a user-friendly summary on completion. The information provided forms a complete view of the application interaction with the system resources, which is typically missing from other profiling and analysis tools. Setting up and running REMORA requires trivial effort, and can be done as a regular user with no special permissions. This enables both users and administrators to download the tool and identify a particular application’s resource requirements within minutes, helping in the diagnosis of errors and performance issues. REMORA is designed to be scalable and have minimal impact on application performance, and includes support for NVIDIA GPUs and Intel Xeon Phi coprocessors. It is open source, modular, and easy to modify to target a large number of HPC resources.