Pipette: Efficient Fine-Grained Reads for SSDs
Journal
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Date Issued
2023-01-01
Author(s)
Bai, Shuhan
Wan, Hu
Huang, Yun
Sun, Xuan
Wu, Fei
Xie, Changsheng
Hsieh, Hung Chih
Xue, Chun Jason
Abstract
Big data applications, such as recommendation system and social network, often generate a huge number of fine-grained reads to the storage. Block-oriented storage devices upon the traditional storage system rely on the paging mechanism to migrate pages to the host DRAM, tending to suffer from these fine-grained read operations in terms of I/O traffic as well as performance. Motivated by this challenge, an efficient fine-grained read framework, Pipette, is proposed in this paper as an extension to the traditional I/O framework. With adaptive design for caching, merging and scheduling, Pipette explores locality and acceleration for fine-grained read requests to establish an efficient byte-granular read path upon the dedicated byte-addressable interface. When the Pipette prototype on an SSD runs popular workloads, we measured throughput gains by up to 50% and 54% with traffic reduction in the range of 41.3× and 56.5×.
Subjects
file system | fine-grained reads | Memory management | Merging | Parallel processing | Performance evaluation | Random access memory | Recommender systems | solid-state drive | Throughput
SDGs
Type
journal article