This project addresses a critical challenge in high-performance computing (HPC): the growing inefficiency and energy demands of moving massive volumes of data between storage and compute units - a bottleneck that hampers scientific progress and national innovation. As the U.S. advances toward exascale computing and increasingly data-centric scientific discovery, the ability to analyze and process large amounts of data efficiently is paramount in nationally significant domains such as energy systems, artificial intelligence (AI), astrophysics, and precision health. This project focuses on Computational Storage Devices (CSDs), a transformative technology that embeds processing power directly into storage hardware, dramatically reducing data movement and enabling faster, more energy-efficient computation. However, programming and optimizing CSDs remains highly complex, limiting their impact. This project develops a scalable, automated software infrastructure (including compilers, runtime systems, and programming interfaces) that makes it practical to deploy and benefit from CSDs across diverse and critical HPC workloads of national importance. By unlocking the potential of near-storage computing, this project directly contributes to national priorities such as AI, energy systems, and advanced manufacturing. Additionally, the project produces open-source tools, CSD-focused benchmarks, and educational resources, contributing to the U.S. leadership in next-generation computing whil