TY - GEN
T1 - MAGE
T2 - 2012 24th International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2012
AU - Li, Sheng
AU - Yoon, Doe Hyun
AU - Chen, Ke
AU - Zhao, Jishen
AU - Ahn, Jung Ho
AU - Brockman, Jay B.
AU - Xie, Yuan
AU - Jouppi, Norman P.
PY - 2012
Y1 - 2012
N2 - Resiliency is one of the toughest challenges in highperformance 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 can adapt memory access granularities and ECC schemes to 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.
AB - Resiliency is one of the toughest challenges in highperformance 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 can adapt memory access granularities and ECC schemes to 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.
UR - https://openalex.org/W4245941243
UR - https://www.scopus.com/pages/publications/84877700379
U2 - 10.1109/SC.2012.73
DO - 10.1109/SC.2012.73
M3 - Conference Paper published in a book
SN - 9781467308069
T3 - International Conference for High Performance Computing, Networking, Storage and Analysis, SC
BT - 2012 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2012
Y2 - 10 November 2012 through 16 November 2012
ER -