TY - GEN
T1 - Minimum-cost data allocation with guaranteed probability on multiple types of memory
AU - Gu, Shouzhen
AU - Zhuge, Qingfeng
AU - Hu, Jingtong
AU - Yi, Juan
AU - Sha, Edwin H.M.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/9/25
Y1 - 2014/9/25
N2 - As the advance of memory technologies, multiple types of memory such as different kinds of non-volatile memory (NVM), SRAM, DRAM, etc. provide a flexible configuration considering performance, energy and cost. For improving the performance of systems with multiple types of memory, data allocation is one of the most important tasks. The previous studies on data allocation problem assume the worst (fixed) case of data-access frequencies. However, the data allocation produced by employing worst case usually leads to an inferior performance for most of time. In this paper, we model this problem by probabilities and design efficient algorithms that can give optimal-cost data allocation with a guaranteed probability. The proposed DAGP algorithm produces a set of feasible data allocation solutions which generates the minimum access time or cost guaranteed by a given probability. The experiments show that our technique can significantly reduce the access time or cost compared with the technique considering worst case scenario. For example, comparing with the optimal result generated by employing the worst cases, our technique can reduce memory access time by 10.35% on average when guaranteed probability is set to be 0.8. Moreover, for 80 percents of cases, memory access time is reduced by 23.98% on average.
AB - As the advance of memory technologies, multiple types of memory such as different kinds of non-volatile memory (NVM), SRAM, DRAM, etc. provide a flexible configuration considering performance, energy and cost. For improving the performance of systems with multiple types of memory, data allocation is one of the most important tasks. The previous studies on data allocation problem assume the worst (fixed) case of data-access frequencies. However, the data allocation produced by employing worst case usually leads to an inferior performance for most of time. In this paper, we model this problem by probabilities and design efficient algorithms that can give optimal-cost data allocation with a guaranteed probability. The proposed DAGP algorithm produces a set of feasible data allocation solutions which generates the minimum access time or cost guaranteed by a given probability. The experiments show that our technique can significantly reduce the access time or cost compared with the technique considering worst case scenario. For example, comparing with the optimal result generated by employing the worst cases, our technique can reduce memory access time by 10.35% on average when guaranteed probability is set to be 0.8. Moreover, for 80 percents of cases, memory access time is reduced by 23.98% on average.
KW - Data Allocation
KW - Guaranteed Probability
KW - Minimum Cost
KW - Multiple types of memory
KW - Non Volatile Memory
UR - https://www.scopus.com/pages/publications/84908608905
U2 - 10.1109/RTCSA.2014.6910510
DO - 10.1109/RTCSA.2014.6910510
M3 - 会议稿件
AN - SCOPUS:84908608905
T3 - RTCSA 2014 - 20th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications
BT - RTCSA 2014 - 20th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2014
Y2 - 20 August 2014 through 22 August 2014
ER -