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20th Annual International Conference on High Performance Computing (2011)
Bengaluru, India
Dec. 18, 2011 to Dec. 21, 2011
ISBN: 978-1-4577-1951-6
pp: 1-11
Karthik Elangovan , Center for Autonomic Computing, Rutgers University, Piscataway NJ, USA
Ivan Rodero , Center for Autonomic Computing, Rutgers University, Piscataway NJ, USA
Manish Parashar , Center for Autonomic Computing, Rutgers University, Piscataway NJ, USA
Francesc Guim , Intel Barcelona, Barcelona, Spain
Isaac Hernandez , Intel Barcelona, Barcelona, Spain
ABSTRACT
The memory subsystem is responsible for a large fraction of the energy consumed by compute nodes in High Performance Computing (HPC) systems. The rapid increase in the number of cores has been accompanied by a corresponding increase in the DRAM capacity and bandwidth, and as a result, the memory system consumes a significant amount of the power budget available to a compute node. Consequently, there is a broad research effort focused on power management techniques using DRAM low-power modes. However, memory power management continues to present many challenges. In this paper, we study the potential of Dynamic Voltage and Frequency Scaling (DVFS) of the memory subsystems, and consider the ability to select different frequencies for different memory channels. Our approach is based on tuning voltage and frequency dynamically to maximize the energy savings while maintaining performance degradation within tolerable limits. We assume that HPC applications do not demand maximum bandwidth throughout the entire period of execution. We can use these low memory demand intervals to tune down the frequency and, as a result, applications can tolerate a reduction in bandwidth to save energy. In this paper, we study application channel access patterns, and use these patterns to determine potential additional energy savings that can be achieved by accordingly controlling the channels independently. We then evaluate the proposed DVFS algorithm using a novel hybrid evaluation methodology that includes simulation as well as executions on real hardware. Our results demonstrate the large potential of adaptive memory power management techniques based on DVFS for HPC workloads.
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CITATION

F. Guim, M. Parashar, I. Rodero, K. Elangovan and I. Hernandez, "Adaptive memory power management techniques for HPC workloads," 2011 18th International Conference on High Performance Computing (HiPC)(HIPC), Bangalore, 2011, pp. 1-11.
doi:10.1109/HiPC.2011.6152740
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