System-level power optimization: techniques and tools
ACM Transactions on Design Automation of Electronic Systems (TODAES)
ASPLOS IX Proceedings of the ninth international conference on Architectural support for programming languages and operating systems
Hardware and Software Techniques for Controlling DRAM Power Modes
IEEE Transactions on Computers
Adaptive Control
An overview of the BlueGene/L Supercomputer
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
The Bladed Beowulf: A Cost-Effective Alternative to Traditional Beowulfs
CLUSTER '02 Proceedings of the IEEE International Conference on Cluster Computing
Power and Energy Profiling of Scientific Applications on Distributed Systems
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Papers - Volume 01
Reducing Power with Performance Constraints for Parallel Sparse Applications
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 11 - Volume 12
Using multiple energy gears in MPI programs on a power-scalable cluster
Proceedings of the tenth ACM SIGPLAN symposium on Principles and practice of parallel programming
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
An overview of energy efficiency techniques in cluster computing systems
Cluster Computing
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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