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Interaction across applications in DRAM memory impacts its energy consumption. This paper makes the case for accurate per-task DRAM energy metering in multicores, which opens new paths to energy/performance optimizations, such as per-task energy-aware task scheduling and energy-aware billing in datacenters. In particular, the contributions of this paper are (i) an ideal per-task energy metering model for DRAM memories; (ii) DReAM, an accurate, yet low cost, implementation of the ideal model (less than 5% accuracy error when 16 tasks share memory); and (iii) a comparison with standard methods (even distribution and access-count based) proving that DReAM is more accurate than these other methods.
CitationLiu, Q. [et al.]. DReAM: Per-task DRAM energy metering in multicore systems. A: International Conference on Parallel and Distributed Computing. "Euro-Par 2014: Parallel Processing 20th International Conference: Porto, Portugal, August 25-29, 2014: proceedings". Porto: Springer, 2014, p. 111-123.
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