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An increasing number of applications benefit from heterogeneous hardware accelerators. Such accelerators often require the application to manually manage memory buffers on devices and transfer data between host and device buffers. A programming model that unifies the virtual address space across the host and devices is appealing because it enables automatic memory transfers and simplifies application-level programming. However, the automatic memory transfers can sometimes be redundant, which decreases performance. NVIDIA’s UVM (unified virtual memory) driver provides a unified virtual address space for CPU-GPU programming. This paper identifies redundant memory transfers (RMTs) as a common performance issue with UVM. To address this issue, this paper proposes a data discard directive, and evaluates two implementations of that directive, UvmDiscard and UvmDiscardLazy. This directive exploits application-level knowledge to avoid RMTs. The implementations were integrated with NVIDIA’s open-source UVM driver to demonstrate their usefulness on real-world CUDA UVM applications. For example, the use of the discard directive increases training throughput by 61.2% on a large deep learning application that oversubscribes GPU memory.