The provided academic paper investigates Shared Virtual Memory (SVM), a technology that integrates GPU memory into host virtual memory systems to improve programming portability and productivity for GPU accelerators. While Unified Memory (UM) aims for transparent data migration, the authors identify that current UM technologies often cause significant performance loss. This research examines the SVM design, analyzes its interactions with applications' data accesses, and quantifies its performance implications across diverse applications, particularly highlighting bottlenecks under memory oversubscription. The study also proposes SVM-aware algorithms and discusses potential design changes to mitigate these performance issues, making it the first comprehensive study of AMD's SVM technology.

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