NVMMU: A Non-volatile Memory Management Unit for Heterogeneous GPU-SSD Architectures

Jie Zhang, D. Donofrio, J. Shalf, M. Kandemir, Myoungsoo Jung
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引用次数: 38

Abstract

Thanks to massive parallelism in modern Graphics Processing Units (GPUs), emerging data processing applications in GPU computing exhibit ten-fold speedups compared to CPU-only systems. However, this GPU-based acceleration is limited in many cases by the significant data movement overheads and inefficient memory management for host-side storage accesses. To address these shortcomings, this paper proposes a non-volatile memory management unit (NVMMU) that reduces the file data movement overheads by directly connecting the Solid State Disk (SSD) to the GPU. We implemented our proposed NVMMU on a real hardware with commercially available GPU and SSD devices by considering different types of storage interfaces and configurations. In this work, NVMMU unifies two discrete software stacks (one for the SSD and other for the GPU) in two major ways. While a new interface provided by our NVMMU directly forwards file data between the GPU runtime library and the I/O runtime library, it supports non-volatile direct memory access (NDMA) that pairs those GPU and SSD devices via physically shared system memory blocks. This unification in turn can eliminate unnecessary user/kernel-mode switching, improve memory management, and remove data copy overheads. Our evaluation results demonstrate that NVMMU can reduce the overheads of file data movement by 95% on average, improving overall system performance by 78% compared to a conventional IOMMU approach.
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NVMMU:用于GPU-SSD异构架构的非易失性内存管理单元
由于现代图形处理单元(GPU)的大规模并行性,GPU计算中的新兴数据处理应用程序与仅使用cpu的系统相比具有十倍的速度。然而,这种基于gpu的加速在许多情况下受到大量数据移动开销和主机端存储访问的低效内存管理的限制。为了解决这些缺点,本文提出了一种非易失性内存管理单元(NVMMU),通过将固态硬盘(SSD)直接连接到GPU来减少文件数据移动开销。通过考虑不同类型的存储接口和配置,我们在使用商用GPU和SSD设备的真实硬件上实现了我们提出的NVMMU。在这项工作中,NVMMU以两种主要方式统一了两个离散的软件堆栈(一个用于SSD,另一个用于GPU)。虽然我们的NVMMU提供的新接口直接在GPU运行时库和I/O运行时库之间转发文件数据,但它支持非易失性直接内存访问(NDMA),通过物理共享系统内存块将GPU和SSD设备配对。这种统一反过来又可以消除不必要的用户/内核模式切换,改进内存管理,并消除数据复制开销。我们的评估结果表明,与传统的IOMMU方法相比,NVMMU可以将文件数据移动的开销平均降低95%,将整体系统性能提高78%。
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Storage Consolidation on SSDs: Not Always a Panacea, but Can We Ease the Pain? AREP: Adaptive Resource Efficient Prefetching for Maximizing Multicore Performance NVMMU: A Non-volatile Memory Management Unit for Heterogeneous GPU-SSD Architectures Scalable Task Scheduling and Synchronization Using Hierarchical Effects Scalable SIMD-Efficient Graph Processing on GPUs
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