Evaluating Memory Energy Efficiency in Parallel I/O Workloads, in Proceedings of IEEE International Conference on Cluster Computing, September, 2007, Austin, TX, pp. 21-30 (Best Paper Award).
Abstract
Power consumption is an important issue for cluster supercomputers as it directly affects their running cost and cooling requirements. This paper investigates the memory energy efficiency of high-end data servers used for supercomputers. Emerging memory technologies allow memory devices to dynamically adjust their power states. To achieve maximum energy saving, the memory management on data servers needs to judiciously utilize these energy-aware devices. As we explore different management schemes under four real-world parallel I/O workloads, we find that the memory energy behavior is determined by a complex interaction among four important factors: (1) cache hit rates that may directly translate performance gain into energy saving, (2) cache populating schemes that perform buffer allocation and affect access locality at the chip level, (3) request clustering that aims to temporally align memory transfers from different buses into the same memory chips, and (4) access patterns in workloads that affect the first three factors.
BibTeX Entry
	@inproceedings{zhu_cluster07, author = {Jianhui Yue and Yifeng Zhu and Zhao Cai}, title = {Evaluating Memory Energy Efficiency in Parallel {I/O} Workloads}, booktitle = {Proceedings of {IEEE} International Conference on Cluster Computing, {Best Paper Award}}, year = {2007}, pages = {21--30}, address = {Austin, TX}, }
Full Paper
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Last modified on October 16, 2007