The lightweight, portable, and flexible nature of containers is driving their widespread adoption in cloud solutions. Data analysis and deep learning applications have especially benefited from containerized solutions. As such data analysis is also being utilized in the high performance computing (HPC) domain, the need for container support in HPC has become paramount. However, container adoption in HPC face crucial performance and I/O challenges. One obstacle is that while there have been container solutions for HPC, such solutions have not been thoroughly investigated, especially from the aspect of their impact on the crucial I/O throughput needs of HPC. To this end, this paper provides a first-of-its-kind empirical analysis of state-of-the-art representative container solutions (Docker, Podman, Singularity, and Charliecloud) in HPC environments, especially how containers interact with the HPC storage systems. We present the design of an analysis framework that is deployed on all nodes in an HPC environment, and captures aspects such as CPU, memory, network, and file I/O statistics from the nodes and the storage system. We are able to garner key insights from our analysis, e.g., Charliecloud outperforms other container solutions in terms of container start-up time, while Singularity and Charliecloud are equivalent in I/O throughput. But this comes at a cost, as Charliecloud invokes the most metadata and I/O operations on the underlying Lustre file system. By identifying such optimization opportunities, we can enhance performance of containers atop HPC and help the aforementioned applications. / Master of Science / Containers are a technology that allow for applications to be packaged along with its ideal environment, all the way down to its preferred operating system. This allows an application to run anywhere that can support containers without a huge hit to the application performance. Hence containers have seen wide adoption for use in the cloud. These qualities have also made it very appealing for use in the world of scientific research in national labs. Modern research heavily relies on the power of computing in order to model, simulate, and test the behavior of real world entities, often making use of large amounts of data and utilizing machine learning and deep learning. Doing this often requires the high performance computing power found in supercomputers. In most cases, scientists just want to be able to write their code and expect it to just work. Their applications might depend on other source code that form part of their standard toolkit and would expect to also be installed in the supercomputing environment. This may not always be the case, taking the scientist's focus away from their work in order ensure their requirements are set up in the supercomputing environment which might require extensive cooperation with the operations team responsible for the supercomputers. Containers easily solve this problem because it can package everything together. However, the use of containers in these environments have not been extensively tested, especially for applications that are very heavy on the analysis of large quantities of data. To fill this gap, this work analyzes the performance of several state-of-the-art container technologies (Docker, Podman, Singularity, Charliecloud), with a particular focus on its interaction with the Lustre data storage systems widely used in supercomputing environments. As part of this work, we design an analysis setup that captures the behavior of various aspects of the high performance computing environment like CPU, memory, network usage and data movement while using containers to run data heavy applications. We garner important insights about their performance that can help inform the best choice of container technology given an environment and the kind of application that needs to be run.