SSDStreamer: Specializing I/O Stack for Large-Scale Machine Learning

Autor:	Jeonghun Gong, Shine Kim, Jonghyun Bae, Wenjing Jin, Hakbeom Jang, Jae W. Lee, Tae Jun Ham, Jaeyoung Jang, Jinkyu Jeong
Rok vydání:	2019
Předmět:	Input/output Random access memory Hardware_MEMORYSTRUCTURES Computer science business.industry Deep learning 02 engineering and technology Machine learning computer.software_genre 020202 computer hardware & architecture Non-volatile memory Stack (abstract data type) Hardware and Architecture Computer cluster Spark (mathematics) 0202 electrical engineering electronic engineering information engineering Artificial intelligence Cache Electrical and Electronic Engineering business computer Software Dram
Zdroj:	IEEE Micro. 39:73-81
ISSN:	1937-4143 0272-1732
DOI:	10.1109/mm.2019.2930497
Popis:	This article presents SSDStreamer, an SSD-based caching system for large-scale machine learning. By using DRAM as stream buffer, instead of an upper-level cache, SSDStreamer significantly outperforms state-of-the-art multilevel caching systems on Apache Spark, while requiring much less DRAM capacity.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::4ce19fa5e3e4ea565bc787af846cc60d https://doi.org/10.1109/mm.2019.2930497 Zobrazit plný text záznamu