Architectural Support for NVRAM Persistence in GPUs
文献类型：期刊论文
| 作者 | Chen, Sui3; Liu, Lei1; Zhang, Weihua2; Peng, Lu3 |
| 刊名 | IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
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| 出版日期 | 2020-05-01 |
| 卷号 | 31期号:5页码:1107-1120 |
| 关键词 | NVRAM persistence GPUs helper warps |
| ISSN号 | 1045-9219 |
| DOI | 10.1109/TPDS.2019.2960233 |
| 英文摘要 | Non-volatile Random Access Memories (NVRAM) have emerged in recent years to bridge the performance gap between the main memory and external storage devices, such as Solid State Drives (SSD). In addition to higher storage density, NVRAM provides byte-addressability, higher bandwidth, near-DRAM latency, and easier access compared to block devices such as traditional SSDs. This enables new programming paradigms taking advantage of durability and larger memory footprint. With the range and size of GPU workloads expanding, NVRAM will present itself as a promising addition to GPU's memory hierarchy. To utilize the non-volatility of NVRAMs, programs should allow durable stores, maintaining consistency through a power loss event. This is usually done through a logging mechanism that works in tandem with a transaction execution layer which can consist of a transactional memory or a locking mechanism. Together, this results in a transaction processing system that preserves the ACID properties. GPUs are designed with high throughput in mind, leveraging high degrees of parallelism. Transactional memory proposals enable fine-grained transactions at the GPU thread-level. However, with lower write bandwidths compared to that of DRAMs, using NVRAM as-is may yield sub-optimal overall system performance when threads experience long latency. 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. Due to the speedup, our proposed method also results in reduction in overall energy consumption. |
| 资助项目 | US National Science Foundation (NSF)[CCF-1422408] ; US National Science Foundation (NSF)[CNS-1527318] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000526526100008 |
| 出版者 | IEEE COMPUTER SOC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/14188]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Chen, Sui |
| 作者单位 | 1.Chinese Acad Sci, SKLCA, Inst Comp Technol, Beijing 100864, Peoples R China 2.Fudan Univ, Software Sch, Shanghai 201203, Peoples R China 3.Louisiana State Univ, Div Elect & Comp Engn, Baton Rouge, LA 70803 USA |
| 推荐引用方式 GB/T 7714 | Chen, Sui,Liu, Lei,Zhang, Weihua,et al. Architectural Support for NVRAM Persistence in GPUs[J]. IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS,2020,31(5):1107-1120. |
| APA | Chen, Sui,Liu, Lei,Zhang, Weihua,&Peng, Lu.(2020).Architectural Support for NVRAM Persistence in GPUs.IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS,31(5),1107-1120. |
| MLA | Chen, Sui,et al."Architectural Support for NVRAM Persistence in GPUs".IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 31.5(2020):1107-1120. |
入库方式： OAI收割
来源：计算技术研究所
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