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.

This work has been partially supported by the Spanish Ministry of Science and Innovation under grant TIN2012-34557, the HiPEAC Network of Excellence, by the European Research Council under the European Union’s 7th FP, ERC Grant Agreement n. 321253, and by a joint study agreement between IBM and BSC (number W1361154). Qixiao Liu has also been funded by the Chinese Scholarship Council under grant 2010608015.