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 where and how power is consumed on high-performance systems and applications. In previous work, we designed a framework called PowerPack that was the first tool to isolate the power consumption of devices including disks, memory, NICs, and processors in a high-performance cluster and correlate these measurements to application functions. In this work, we extend our framework to support systems with multicore, multiprocessor-based nodes, and then provide in-depth analyses of the energy consumption of parallel applications on clusters of these systems. These analyses include the impacts of chip multiprocessing on power and energy efficiency, and its interaction with application executions. In addition, we use PowerPack to study the power dynamics and energy efficiencies of dynamic voltage and frequency scaling (DVFS) techniques on clusters. Our experiments reveal conclusively how intelligent DVFS scheduling can enhance system energy efficiency while maintaining performance.Index Terms-Distributed system, CMP-based cluster, energy efficiency, power measurement, system tools, power management, dynamic voltage and frequency scaling. Ç 658

Preventing and controlling outbreaks of infectious diseases such as pandemic influenza is a top public health priority. We describe EpiSimdemics -a scalable parallel algorithm to simulate the spread of contagion in large, realistic social contact networks using individual-based models. EpiSimdemics is an interaction-based simulation of a certain class of stochastic reaction-diffusion processes. Straightforward simulations of such process do not scale well, limiting the use of individual-based models to very small populations. EpiSimdemics is specifically designed to scale to social networks with 100 million individuals. The scaling is obtained by exploiting the semantics of disease evolution and disease propagation in large networks. We evaluate an MPI-based parallel implementation of EpiSimdemics on a mid-sized HPC system, demonstrating that EpiSimdemics scales well. EpiSimdemics has been used in numerous sponsor defined case studies targeted at policy planning and course of action analysis, demonstrating the usefulness of EpiSimdemics in practical situations.

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The photosynthetic green sulfur bacterium Chlorobaculum tepidum assimilates CO 2 and organic carbon sources (acetate or pyruvate) during mixotrophic growth conditions through a unique carbon and energy metabolism. Using a 13 C-labeling approach, this study examined biosynthetic pathways and flux distributions in the central metabolism of C. tepidum. The isotopomer patterns of proteinogenic amino acids revealed an alternate pathway for isoleucine synthesis (via citramalate synthase, CimA, CT0612). A 13 C-assisted flux analysis indicated that carbons in biomass were mostly derived from CO 2 fixation via three key routes: the reductive tricarboxylic acid (RTCA) cycle, the pyruvate synthesis pathway via pyruvate: ferredoxin oxidoreductase, and the CO 2 -anaplerotic pathway via phosphoenolpyruvate carboxylase. During mixotrophic growth with acetate or pyruvate as carbon sources, acetyl-CoA was mainly produced from acetate (via acetyl-CoA synthetase) or citrate (via ATP citrate lyase). Pyruvate:ferredoxin oxidoreductase converted acetyl-CoA and CO 2 to pyruvate, and this growth-rate control reaction is driven by reduced ferredoxin generated during phototrophic growth. Most reactions in the RTCA cycle were reversible. The relative fluxes through the RTCA cycle were 80ϳ100 units for mixotrophic cultures grown on acetate and 200ϳ230 units for cultures grown on pyruvate. Under the same light conditions, the flux results suggested a trade-off between energy-demanding CO 2 fixation and biomass growth rate; C. tepidum fixed more CO 2 and had a higher biomass yield (Y X/S , mole carbon in biomass/mole substrate) in pyruvate culture (Y X/S ‫؍‬ 9.2) than in acetate culture (Y X/S ‫؍‬ 6.4), but the biomass growth rate was slower in pyruvate culture than in acetate culture.Chlorobaculum tepidum is a representative green sulfur bacterium that is ecologically significant in global cycling of carbon, nitrogen, and sulfur (1, 2). The C. tepidum genome has been sequenced, and the genetic tools for creating C. tepidum mutant strains have been developed to make transposon-based mutations or targeted gene disruptions, which offer great potential to engineer C. tepidum for future applications (3). The annotated genome reveals unique aspects in carbon and energy metabolism in C. tepidum. Instead of using the Calvin-Benson cycle for CO 2 assimilation as in most photosynthetic organisms, C. tepidum captures energy from light and uses it along with electrons, primarily derived from oxidation of sulfur compounds, to drive the reductive tricarboxylic acid cycle (RTCA) 3 for synthesis of building block molecules (3). C. tepidum can grow mixotrophically with acetate or pyruvate as the organic carbon source (2). Although recent research has been performed on the carbon and energy metabolism of C. tepidum (4, 5), rigorous quantification of the metabolic pathway activities has not yet been achieved. To provide quantitative readout of the metabolic functions and regulatory mechanisms, this study has performed 13 C-assisted metabolic flux analysis of ...