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Energy efficiency is a major concern in modern high-performance computing system design. In the past few years, there has been mounting evidence that power usage limits system scale and computing density, and thus, ultimately system performance. However, despite the impact of power and energy on the computer systems community, few studies provide insight to(More)
Performance and power are critical design constraints in today's high-end computing systems. Reducing power consumption without impacting system performance is a challenge for the HPC community. We present a runtime system (CPU MISER) and an integrated performance model for performance-directed, power-aware cluster computing. CPU MISER supports system-wide,(More)
Left unchecked, the fundamental drive to increase peak performance using tens of thousands of power hungry components will lead to intolerable operating costs and failure rates. High-performance, power-aware distributed computing reduces power and energy consumption of distributed applications and systems without sacrificing performance. Generally, we use(More)
Power consumption is a troublesome design constraint for emergent systems such as IBM's BlueGene /L. If current trends continue, future petaflop systems will require 100 megawatts of power to maintain high-performance. To address this problem the power and energy characteristics of high-performance systems must be characterized. To date, power-performance(More)
MapReduce is a programming model for data intensive computing on large-scale distributed systems. With its wide acceptance and deployment, improving the energy efficiency of MapReduce will lead to significant energy savings for data centers and computational grids. In this paper, we study the performance and energy efficiency of the Hadoop implementation of(More)
Left unchecked, the fundamental drive to increase peak performance using tens of thousands of power hungry components will lead to intolerable operating costs and failure rates. Recent work has shown application characteristics of single-processor, memory-bound non-interactive codes and distributed, interactive Web services can be exploited to conserve(More)
Power-aware processors operate in various power modes to reduce energy consumption with a corresponding decrease in peak processor throughput. Recent work has shown power-aware clusters can conserve significant energy (>30%) with minimal performance loss (<1%) running parallel scientific workloads. Nonetheless, such savings are typically achieved(More)
In many applications, attribute and relationship data areavailable, carrying complementary information about real world entities. In such cases, a joint analysis of both types of data can yield more accurate results than classical clustering algorithms that either use only attribute data or only relationship (graph) data. The Connected k-Center (CkC) has(More)
Attribute data and relationship data are two principle types of data, representing the intrinsic and extrinsic properties of entities. While attribute data has been the main source of data for cluster analysis, relationship data such as social networks or metabolic networks are becoming increasingly available. It is also common to observe both data types(More)