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GreenDIMM: OS-Assisted DRAM Power Management for DRAM with a Sub-Array Granularity Power-Down State

Title
GreenDIMM: OS-Assisted DRAM Power Management for DRAM with a Sub-Array Granularity Power-Down State
Author(s)
Lee, SeunghakKang, Ki DongLee, HwanjunPark, HyungwonSon, YounghoonKim, NamsunKim, Daehoon
Issued Date
2021-10-20
Citation
54th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), pp.131 - 142
Type
Conference Paper
ISBN
9781450385572
ISSN
1072-4451
Abstract
Power and energy consumed byDRAMcomprising main memory of data-center servers have increased substantially as the capacity and bandwidth of memory increase. Especially, the fraction of DRAM background power in DRAM total power is already high, and it will continue to increase with the decelerating DRAM technology scaling as we will have to plug more DRAM modules in servers or stack more DRAM dies in a DRAM package to provide necessary DRAM capacity in the future. To reduce the background power, we may exploit low average utilization of the DRAM capacity in data-center servers (i.e., 40 C60%) for DRAM power management. Nonetheless, the current DRAM power management supports lowpower states only at the rank granularity, which becomes ineffective with memory interleaving techniques devised to disperse memory requests across ranks. That is, ranks need to be frequently woken up from low-power states with aggressive power management, which can significantly degrade system performance, or they do not get a chance to enter low-power states with conservative power management. To tackle such limitations of the current DRAM power management, we propose GreenDIMM, OS-assisted DRAM power management. Specifically, GreenDIMM first takes a memory block in physical address space mapped to a group of DRAM sub-arrays across every channel, rank, and bank as a unit of DRAM power management. This facilitates fine-grained DRAM power management while keeping the benefit of memory interleaving techniques. Second, GreenDIMM exploits memory on-/off-lining operations of the modern OS to dynamically remove/add memory blocks from/to the physical address space, depending on the utilization of memory capacity at run-time. Third, GreenDIMM implements a deep powerdown state at the sub-array granularity to reduce the background power of the off-lined memory blocks. As the off-lined memory blocks are removed from the physical address space, the sub-arrays will not receive any memory request and stay in the power-down state until the memory blocks are explicitly on-lined by the OS. Our evaluation with a commercial server running diverse workloads shows that GreenDIMM can reduce DRAM and system power by 36% and 20%, respectively, with ~1% performance degradation. © 2021 Association for Computing Machinery.
URI
http://hdl.handle.net/20.500.11750/46891
DOI
10.1145/3466752.3480089
Publisher
IEEE Computer Society
Related Researcher
  • 김대훈 Kim, Daehoon
  • Research Interests Computer Architecture and Systems; Virtualization; Cloud Computing
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Appears in Collections:
Department of Electrical Engineering and Computer Science Computer Architecture and Systems Lab 2. Conference Papers

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