Efficient GPU NVRAM Persistence with Helper Warps
Sui Chen, Faen Zhang, Lei Liu, Lu Peng · 2019
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.