High performance computing is an important asset to scientific research, enabling the study of phenomena such as nuclear physics or climate change, that are difficult or impossible to be studied in traditional experiments or allowing researchers to utilize large amounts of data from experiments such as the Large Hadron Collider. No matter the use of HPC, the need for performance is always present; however, the fast-changing nature of computer systems means that software must be continually updated to run efficiently on the newest machines. In this paper, we discuss methods and tools used to understand the performance of an application running on HPC systems and how this understanding can translate into improved performance. We primarily focus on node-level issues, but also mention some of the basic issues involved with multi-node analysis as well.