Securing GPU via region-based bounds checking

Jaewon Lee, Yonghae Kim, Jiashen Cao, Euna Kim, Jaekyu Lee, Hyesoon Kim
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引用次数: 4

Abstract

Graphics processing units (GPUs) have become essential general-purpose computing platforms to accelerate a wide range of workloads, such as deep learning, scientific, and high-performance computing (HPC) applications. However, recent memory corruption attacks, such as buffer overflow, exposed security vulnerabilities in GPUs. We demonstrate that out-of-bounds writes are reproducible on an Nvidia GPU, which can enable other security attacks. We propose GPUShield, a hardware-software cooperative region-based bounds-checking mechanism, to improve GPU memory safety for global, local, and heap memory buffers. To achieve effective protection, we update the GPU driver to assign a random but unique ID to each buffer and local variable and store individual bounds information in the bounds table allocated in the global memory. The proposed hardware performs efficient bounds checking by indexing the bounds table with unique IDs. We further reduce the bounds-checking overhead by utilizing compile-time bounds analysis, workgroup/warp-level bounds checking, and GPU-specific address mode. Our performance evaluations show that GPUShield incurs little performance degradation across 88 CUDA benchmarks on the Nvidia GPU architecture and 17 OpenCL benchmarks on the Intel GPU architecture with a marginal hardware overhead.
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通过基于区域的边界检查来保护GPU
图形处理单元(gpu)已经成为必不可少的通用计算平台,可以加速各种工作负载,如深度学习、科学和高性能计算(HPC)应用。然而,最近的内存损坏攻击,如缓冲区溢出,暴露了gpu的安全漏洞。我们证明了越界写入在Nvidia GPU上是可重复的,这可以使其他安全攻击成为可能。我们提出了GPUShield,一种基于硬件和软件的基于区域的边界检查机制,以提高全局、本地和堆内存缓冲区的GPU内存安全性。为了实现有效的保护,我们更新GPU驱动程序,为每个缓冲区和局部变量分配一个随机但唯一的ID,并将各个边界信息存储在全局内存中分配的边界表中。提议的硬件通过用唯一id索引边界表来执行有效的边界检查。通过利用编译时边界分析、工作组/warp级边界检查和gpu特定的地址模式,我们进一步减少了边界检查开销。我们的性能评估表明,GPUShield在Nvidia GPU架构上的88个CUDA基准测试和Intel GPU架构上的17个OpenCL基准测试中几乎没有性能下降,硬件开销很小。
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