David K. Newsom

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One of the key challenges in optimizing CPU power consumption at the program level is the difficulty of precisely measuring the power consumption of the CPU (as distinct from other system components) during the various phases of a program's execution. This paper presents a scalable CPU power measurement framework with its associated reporting and data(More)
Reducing energy consumption without affecting computational performance is a significant research driver in computer engineering. The Partitioned Global Address Space (PGAS) programming model provides a global address space for ease-of-use while providing locality-awareness for efficient execution. For symmetric multiprocessor (SMP) clusters, PGAS(More)
Power consumption increasingly presents an upper bound on sustainable large scale computing performance and reliability. The Partitioned Global Address Space (PGAS) programming model is a family of parallel programming paradigms with a global address space for ease-of-use while providing locality awareness for efficient execution. Very little exploration(More)
Research in high performance computing (HPC) energy optimization is a growing field motivated by cost and environmental drivers. As commodity server platforms are increasingly deployed as affordably scalable compute clusters, the processor and operating system’s energy management capabilities also continues to advance in sophistication. This trend creates a(More)
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