SCHEDULE: NOV 10-16, 2012
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MAGE - Adaptive Granularity and ECC for Resilient and Power Efficient Memory Systems
SESSION: DRAM Power and Resiliency Management
EVENT TYPE: Papers
TIME: 4:00PM - 4:30PM
SESSION CHAIR: Nathan Debardeleben
AUTHOR(S):Sheng Li, Doe Hyun Yoon, Ke Chen, Jishen Zhao, Jung Ho Ahn, Jay Brockman, Yuan xie, Norman Jouppi
ROOM:355-D
ABSTRACT:
Resiliency is one of the toughest challenges in high performance computing, and memory accounts for a significant fraction of errors. Providing strong error tolerance in memory usually requires a wide memory channel that incurs a large access granularity (hence, a large cache line). Unfortunately, applications with limited spatial locality waste memory power and bandwidth on systems with a large access granularity. Thus, careful design considerations must be made to balance memory system performance, power efficiency, and resiliency.
In this paper, we propose MAGE, a Memory system with Adaptive Granularity and ECC, to achieve high performance, power efficiency, and resiliency. MAGE enables adaptive selection of appropriate granularities and ECC schemes for applications with different memory behaviors. Our experiments show that MAGE achieves more than a 28% energy-delay product improvement, compared to the best existing systems with static granularity and ECC.
Chair/Author Details:
Nathan Debardeleben (Chair) - Los Alamos National Laboratory
Sheng Li - Hewlett-Packard
Doe Hyun Yoon - Hewlett-Packard
Ke Chen - University of Notre Dame
Jishen Zhao - Pennsylvania State University
Jung Ho Ahn - Seoul National University
Jay Brockman - University of Notre Dame
Yuan xie - Pennsylvania State University
Norman Jouppi - Hewlett-Packard
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MAGE - Adaptive Granularity and ECC for Resilient and Power Efficient Memory Systems
SESSION: DRAM Power and Resiliency Management
EVENT TYPE:
TIME: 4:00PM - 4:30PM
SESSION CHAIR: Nathan Debardeleben
AUTHOR(S):Sheng Li, Doe Hyun Yoon, Ke Chen, Jishen Zhao, Jung Ho Ahn, Jay Brockman, Yuan xie, Norman Jouppi
ROOM:355-D
ABSTRACT:
Resiliency is one of the toughest challenges in high performance computing, and memory accounts for a significant fraction of errors. Providing strong error tolerance in memory usually requires a wide memory channel that incurs a large access granularity (hence, a large cache line). Unfortunately, applications with limited spatial locality waste memory power and bandwidth on systems with a large access granularity. Thus, careful design considerations must be made to balance memory system performance, power efficiency, and resiliency.
In this paper, we propose MAGE, a Memory system with Adaptive Granularity and ECC, to achieve high performance, power efficiency, and resiliency. MAGE enables adaptive selection of appropriate granularities and ECC schemes for applications with different memory behaviors. Our experiments show that MAGE achieves more than a 28% energy-delay product improvement, compared to the best existing systems with static granularity and ECC.
Chair/Author Details:
Nathan Debardeleben (Chair) - Los Alamos National Laboratory
Sheng Li - Hewlett-Packard
Doe Hyun Yoon - Hewlett-Packard
Ke Chen - University of Notre Dame
Jishen Zhao - Pennsylvania State University
Jung Ho Ahn - Seoul National University
Jay Brockman - University of Notre Dame
Yuan xie - Pennsylvania State University
Norman Jouppi - Hewlett-Packard
Click here to download .ics calendar file