{"title":"三维闪存层RBER变化感知读取性能优化","authors":"Shiqiang Nie, Youtao Zhang, Weiguo Wu, Jun Yang","doi":"10.1109/DAC18072.2020.9218631","DOIUrl":null,"url":null,"abstract":"3D NAND flash enables the construction of large capacity Solid-State Drives (SSDs) for modern computer systems. While effectively reducing per bit cost, 3D NAND flash exhibits non-negligible process variations and thus RBER (raw bit error rate) difference across layers, which leads to sub-optimal read performance for applications with either small or large I/O requests. In this paper, we propose LRR, Layer RBER variation aware Read optimization schemes, to address the challenge. LRR consists of two schemes — LRR subpage read scheduling (SRS) and LRR fullpage allocation (FPA). SRS groups small read requests from the layers with similar RBERs to reduce the average read latency of subpage sized read requests. FPA distributes the data of a large write to multiple layers, which improves the read latency when reading from layers with large RBERs. Our experimental results show that our proposed scheme LRR reduces 46% read latency on average over the state-of-the-art.","PeriodicalId":428807,"journal":{"name":"2020 57th ACM/IEEE Design Automation Conference (DAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Layer RBER Variation Aware Read Performance Optimization for 3D Flash Memories\",\"authors\":\"Shiqiang Nie, Youtao Zhang, Weiguo Wu, Jun Yang\",\"doi\":\"10.1109/DAC18072.2020.9218631\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"3D NAND flash enables the construction of large capacity Solid-State Drives (SSDs) for modern computer systems. While effectively reducing per bit cost, 3D NAND flash exhibits non-negligible process variations and thus RBER (raw bit error rate) difference across layers, which leads to sub-optimal read performance for applications with either small or large I/O requests. In this paper, we propose LRR, Layer RBER variation aware Read optimization schemes, to address the challenge. LRR consists of two schemes — LRR subpage read scheduling (SRS) and LRR fullpage allocation (FPA). SRS groups small read requests from the layers with similar RBERs to reduce the average read latency of subpage sized read requests. FPA distributes the data of a large write to multiple layers, which improves the read latency when reading from layers with large RBERs. Our experimental results show that our proposed scheme LRR reduces 46% read latency on average over the state-of-the-art.\",\"PeriodicalId\":428807,\"journal\":{\"name\":\"2020 57th ACM/IEEE Design Automation Conference (DAC)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 57th ACM/IEEE Design Automation Conference (DAC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DAC18072.2020.9218631\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 57th ACM/IEEE Design Automation Conference (DAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DAC18072.2020.9218631","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
摘要
3D NAND闪存能够为现代计算机系统构建大容量固态硬盘(ssd)。在有效降低每比特成本的同时,3D NAND闪存显示出不可忽略的过程变化,从而导致不同层之间的RBER(原始误码率)差异,这导致无论是小I/O请求还是大I/O请求的应用程序的读取性能都不理想。在本文中,我们提出了LRR,层RBER变化感知读取优化方案,以解决这一挑战。LRR包括两种方案:LRR子页读取调度(SRS)和LRR全页分配(FPA)。SRS将来自具有相似rber的层的小读请求分组,以减少子页面大小的读请求的平均读延迟。FPA将大的写数据分布到多个层,从而提高了从rber大的层读时的读时延。我们的实验结果表明,我们提出的LRR方案比最先进的方案平均减少了46%的读延迟。
Layer RBER Variation Aware Read Performance Optimization for 3D Flash Memories
3D NAND flash enables the construction of large capacity Solid-State Drives (SSDs) for modern computer systems. While effectively reducing per bit cost, 3D NAND flash exhibits non-negligible process variations and thus RBER (raw bit error rate) difference across layers, which leads to sub-optimal read performance for applications with either small or large I/O requests. In this paper, we propose LRR, Layer RBER variation aware Read optimization schemes, to address the challenge. LRR consists of two schemes — LRR subpage read scheduling (SRS) and LRR fullpage allocation (FPA). SRS groups small read requests from the layers with similar RBERs to reduce the average read latency of subpage sized read requests. FPA distributes the data of a large write to multiple layers, which improves the read latency when reading from layers with large RBERs. Our experimental results show that our proposed scheme LRR reduces 46% read latency on average over the state-of-the-art.