DocumentCode
2271712
Title
Transforming memory systems: Optimizing for client value on emerging workloads
Author
Nowka, Kevin J.
Author_Institution
IBM Res. - Austin, Austin, TX, USA
fYear
2012
fDate
23-25 April 2012
Firstpage
1
Lastpage
2
Abstract
Computing systems are increasingly being transformed to better satisfy the demands of cloud computing, Big Data, and deep, sophisticated analytics applications. These applications are driving an explosion in volume of data, acceleration of the rate at which this data must be consumed, and an increase in the diversity of sources of data. Memory system architectures and designs are perhaps most affected by these changes in computing applications. The disruptive trends resulting from these new application spaces lead to significant capacity, power, and cost pressures on computing systems. These trends will lead to changes in traditional memory technologies and memory systems and represent an opportunity of new memory technologies and organizations. Storage class memory is particularly suited to a significant set of these application spaces.
Keywords
memory architecture; big data; client value; cloud computing; computing systems; emerging workloads; memory system architectures; memory system design; sophisticated analytics applications; storage class memory; Abstracts; Memory architecture; Random access memory;
fLanguage
English
Publisher
ieee
Conference_Titel
VLSI Design, Automation, and Test (VLSI-DAT), 2012 International Symposium on
Conference_Location
Hsinchu
ISSN
PENDING
Print_ISBN
978-1-4577-2080-2
Type
conf
DOI
10.1109/VLSI-DAT.2012.6212609
Filename
6212609
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