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Efficient GPU NVRAM Persistence with Helper Warps

机译:高效的GPU NVRAM持久性与帮助者扭曲

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Non-volatile Random-Access Memories (NVRAM) have emerged in recent years to bridge the performance gap between the main memory and external storage devices. To utilize the non-volatility of NVRAMs, programs should allow durable stores, meaning consistency must be maintained during a power loss event. GPUs are designed with high throughput, leveraging high degrees of parallelism. However, with lower NVRAM write bandwidths compared to that of DRAMs, using NVRAM as is may yield suboptimal overall system performance. To address this problem, we propose using Helper Warps to move persistence out of the critical path of transaction execution, alleviating the impact of latencies. Our mechanism achieves a speedup of 4.4 and 1.5 under bandwidth limits of 1.6 GB/s and 12 GB/s and is projected to maintain speed advantage even when NVRAM bandwidth gets as high as hundreds of GB/s in certain cases.
机译:近年来出现了非易失性随机接入存储器(NVRAM)以弥合主存储器和外部存储设备之间的性能差距。为了利用NVRAM的非波动性,程序应允许耐用的商店,这意味着必须在电源丢失事件期间保持一致性。 GPU设计具有高吞吐量,利用高度的平行度。但是,与DRAM相比,利用NVRAM写入带宽,使用NVRAM可以产生次优整体系统性能。为了解决这个问题,我们建议使用帮助者扭曲来持续退出交易执行的关键路径,减轻延迟的影响。我们的机制实现了1.6 GB / s和12 GB / s的带宽限制下的4.4和1.5的加速,并预计即使在某些情况下NVRAM带宽高达数百GB / s,也会预计保持速度优势。

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