This project addresses the growing challenge of data movement in high-performance computing systems with diverse processors, memory, storage, and networks. These systems are critical for national-scale efforts in drug discovery, materials science, energy research, and large-scale artificial intelligence and machine learning. Applications such as molecular dynamics, graph neural networks, and particle-in-cell simulations generate large data volumes that must be moved efficiently. Data transfers often limit performance rather than computation. This project develops tools to reduce data movement time and energy, improving throughput, efficiency, and scientific productivity. It empowers researchers and developers to scale workflows on complex HPC systems while fostering collaboration among academia, industry, and national laboratories to transition ideas into practical solutions for exascale platforms. The project also advances national interests by enabling scalable AI and simulation workflows and engaging students in systems research for next-generation infrastructure. The project develops a unified framework to reduce data movement overheads in heterogeneous high-performance computing systems. It integrates three core components: a cross-layer monitoring and learning framework that characterizes data transfer patterns and predicts contention; a heterogeneity-aware data movement scheduler that coordinates bandwidth usage across computation, memory, storage, and interconne