Let-Me-In: (Still) Employing In-pointer Bounds Metadata for Fine-grained GPU Memory Safety
Jaewon Lee, Euijun Chung, Saurabh Singh, Seonjin Na, Yonghae Kim, Jaekyu Lee, Hyesoon Kim
Abstract
The importance of ensuring the robustness of GPU systems has grown significantly, especially as GPUs have become vital in critical decision-making systems such as autonomous driving and medical diagnostics. However, GPU programming languages, primarily based on , inherit memory vulnerabilities that threaten the robustness of GPU applications. The heterogeneous GPU memory hierarchy makes it more difficult to find effective universal solutions. While several studies have proposed advanced GPU memory safety mechanisms, they still grapple with significant challenges, including substantial metadata storage and access overhead, elevated hardware implementation costs, and limited security coverage, particularly regarding fine-grained memory safety. We address this issue with Let-Me-In (LMI), a fine-grained memory safety mechanism specifically designed for GPUs. LMI features an efficient hardware bounds-checking mechanism that ensures negligible impact on performance and hardware costs, even in scenarios where thousands of concurrent threads perform memory operations across buffers in heap and local memory. This is achieved by aligning memory allocation to powers of two and performing static analysis to identify and mark pointer arithmetic instructions. This approach also enables storing metadata inside the unused upper bits of pointers, which are shrinking due to the expansion of the virtual memory address space. The unique characteristics of GPU programs make this approach feasible, unlike in CPU programs, where the inherent complexity of programs poses challenges. Our evaluation shows that LMI incurs only negligible hardware and performance overhead, making it a practical and efficient solution for enhancing GPU memory safety.
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