Files in this item

FilesDescriptionFormat

application/pdf

application/pdfECE499-Sp2017-vemulapati.pdf (2MB)Restricted to U of Illinois
(no description provided)PDF

Description

Title:Page migration and placement in hybrid memory systems using machine learning algorithms
Author(s):Vemulapati, Vibhakar
Contributor(s):Kim, Nam Sung
Subject(s):hybrid memory system
phase change memory
prefetching
machine learning
Abstract:As we reach the end of DRAM technology scaling, the prevalence of new memory technology in computers is inevitable. Phase-change memory (PCM) is an emerging non-volatile memory technology which can be denser than existing DRAM cells, but is slower. A possible solution is a hybrid PCM/DRAM memory system where we have a large capacity PCM and a DRAM used as a buffer between the processor and PCM. We will investigate various page migration algorithms on the hybrid system to maximize utilization of faster DRAM to mitigate the performance slowdown associated with using PCM as main memory. We primarily investigate the effects of speculative pre-fetching of pages in memory from PCM to DRAM using machine learning algorithms. The page rank program that we tested had irregular memory access patterns that made it difficult to predict the pages that had to be pre-fetched, causing performance slowdown when compared to running the program without modification. The overhead of predicting the prefetch page far outweighed the performance increase achieved.
Issue Date:2017-12
Genre:Other
Type:Text
Language:English
URI:http://hdl.handle.net/2142/97887
Date Available in IDEALS:2017-08-28


This item appears in the following Collection(s)

Item Statistics