Exploring Energy-Time Tradeoff in High Performance Computing
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Date
2005-05-16
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Abstract
Recently, energy has become an important issue in high-performance computing. For example, low power/energy supercomputers, such as Green Destiny, have been built; the idea is to increase the energy efficiency of nodes. However, these clusters tend to save energy at the expense of performance. Our approach is instead to use high-performance cluster nodes with frequency scalable AMD-64 processors; energy can be saved by scaling down the CPU. Our cluster provides a different balance of power and performance than low-power machines such as Green Destiny. In particular, its performance is on par with a Pentium 4-equipped cluster.
This thesis investigates the energy consumption and execution time of a wide range of applications, both serial and parallel, on a power-scalable cluster. We study via direct measurement both intra-node and inter-node effects of memory and communication bottlenecks, respectively. Additionally, we present a framework for executing a single application in several frequency-voltage settings. The basic idea is to first divide programs in to phases and then execute a series of experiments, with each phase assigned a prescribed frequency.
Our results show that a power-scalable cluster has the potential to save energy by scaling the processor down to lower energy levels. Furthermore, we found that for some programs, it is possible to both consume less energy and execute in less time by increasing number of nodes and reducing frequency-voltage setting of the nodes. Additionally, we found that our phase detecting heuristic can find assignments of frequency to phase that is superior to any fixed-frequency solution.
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Dynamic Voltage Scaling, Power-Aware Computing
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Degree
MS
Discipline
Computer Science
