Memory-Miser: A Performance-Constrained Runtime System for Power-Scalable Clusters

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Conference Proceeding


Main memory in clusters may dominate total system power. The resulting energy consumption increases system operating cost and the heat produced reduces reliability. Emergent memory technology will provide servers with the ability to dynamically turn-on (online) and turn-off (offline) memory devices at runtime. This technology, coupled with slack in memory demand, offers the potential for significant energy savings in clusters of servers. Enabling power-aware memory and conserving energy in clusters are non-trivial. First, power-aware memory techniques must be scalable to thousands of devices. Second, techniques must not negatively impact the performance of parallel scientific applications. Third, techniques must be transparent to the user to be practical. We propose a Memory Management Infra-Structure for Energy Reduction (Memory MISER). Memory MISER is transparent, performance-neutral, and scalable. It consists of a prototype Linux kernel that manages memory at device granularity and a userspace daemon that monitors memory demand systemically to control devices and implement energy- and performance-constrained policies. Experiments on an 8-node cluster show our control daemon reduces memory energy up to 56.8% with <1% performance degradation for several classes of parallel scientific codes. Our daemon uses a PID controller to conservatively offline memory and aggressively online memory at runtime. For multi-user workloads where memory demand often spikes dramatically, Memory MISER can save up to 67.94% of memory energy with <1% performance degradation. Current IBM eServer systems support up to 2 terabytes of SDRAM per node and 16 processors. For a server-based cluster with 8 90-watt processors and 32 GB of SDRAM per processor, Memory MISER can save about 30% total system energy for multi-user parallel workloads.

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Proceedings of the 4th International Conference on Computing Frontiers

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