Energy - and Heat-aware HPC Benchmarks

International audienceTo evaluate data centers is tough. Several metrics are available to provide insight into their behaviour, but usually they are tested using simple benchmarks like LINPACK for HPC oriented data centers. A good choice of benchmarks is necessary to evaluate all the impact of applications on those data centers. One point that is often overlooked is their energy- and thermal-quality. To evaluate these qualities, adequate benchmarks are required from several points of view: from the nodes to the whole building. Classical benchmarks selection mainly focuses on time and raw performance. This article aims at shifting the focus towards an energy- and power-point of view. To this end, we select benchmarks able to evaluate data centers not only from this performance perspective, but also from the energy and thermal standpoint. We also provide insight into several classical benchmarks and method to select an adequate and small number of benchmarks in order to provide a sensible and minimum set of energy- and thermal-aware benchmarks for HPC systems

Modeling Data Center Building Blocks for Energy-efficiency and Thermal Simulations

International audienceIn this paper we present a concept and specification of Data Center Efficiency Building Blocks (DEBBs), which represent hardware components of a data center complemented by descriptions of their energy efficiency. Proposed building blocks contain hardware and thermodynamic models that can be applied to simulate a data center and to evaluate its energy efficiency. DEBBs are available in an open repository being built by the CoolEmAll project. In the paper we illustrate the concept by an example of DEBB defined for the RECS multi-server system including models of its power usage and thermodynamic properties. We also show how these models are affected by specific architecture of modeled hardware and differences between various classes of applications. Proposed models are verified by a comparison to measurements on a real infrastructure. Finally, we demonstrate how DEBBs are used in data center simulations

Energy-Efficient, Thermal-Aware Modeling and Simulation of Datacenters: The CoolEmAll Approach and Evaluation Results

International audienceThis paper describes the CoolEmAll project and its approach for modeling and simulating energy-efficient and thermal-aware data centers. The aim of the project was to address energy-thermal efficiency of data centers by combining the optimization of IT, cooling and workload management. This paper provides a complete data center model considering the workload profiles, the applications profiling, the power model and a cooling model. Different energy efficiency metrics are proposed and various resource management and scheduling policies are presented. The proposed strategies are validated through simulation at different levels of a data cente

Energy and thermal models for simulation of workload and resource management in computing systems

In the recent years, we have faced the evolution of high-performance computing (HPC) systems towards higher scale, density and heterogeneity. In particular, hardware vendors along with software providers, HPC centers, and scientists are struggling with the exascale computing challenge. As the density of both computing power and heat is growing, proper energy and thermal management becomes crucial in terms of overall system efficiency. Moreover, an accurate and relatively fast method to evaluate such large scale computing systems is needed. In this paper we present a way to model energy and thermal behavior of computing system. The proposed model can be used to effectively estimate system performance, energy consumption, and energy-efficiency metrics. We evaluate their accuracy by comparing the values calculated based on these models against the measurements obtained on real hardware. Finally, we show how the proposed models can be applied to workload scheduling and resource management in large scale computing systems by integrating them in the DCworms simulation framework

Morpholino Gene Knockdown in Adult Fundulus heteroclitus: Role of SGK1 in Seawater Acclimation

The Atlantic killifish (Fundulus heteroclitus) is an environmental sentinel organism used extensively for studies on environmental toxicants and salt (NaCl) homeostasis. Previous research in our laboratory has shown that rapid acclimation of killifish to seawater is mediated by trafficking of CFTR chloride channels from intracellular vesicles to the plasma membrane in the opercular membrane within the first hour in seawater, which enhances chloride secretion into seawater, thereby contributing to salt homeostasis. Acute transition to seawater is also marked by an increase in both mRNA and protein levels of serum glucocorticoid kinase 1 (SGK1) within 15 minutes of transfer. Although the rise in SGK1 in gill and its functional analog, the opercular membrane, after seawater transfer precedes the increase in membrane CFTR, a direct role of SGK1 in elevating membrane CFTR has not been established in vivo. To test the hypothesis that SGK1 mediates the increase in plasma membrane CFTR we designed two functionally different vivo-morpholinos to knock down SGK1 in gill, and developed and validated a vivo-morpholino knock down technique for adult killifish. Injection (intraperitoneal, IP) of the splice blocking SGK1 vivo-morpholino reduced SGK1 mRNA in the gill after transition from fresh to seawater by 66%. The IP injection of the translational blocking and splice blocking vivo-morpholinos reduced gill SGK1 protein abundance in fish transferred from fresh to seawater by 64% and 53%, respectively. Moreover, knock down of SGK1 completely eliminated the seawater induced rise in plasma membrane CFTR, demonstrating that the increase in SGK1 protein is required for the trafficking of CFTR from intracellular vesicles in mitochondrion rich cells to the plasma membrane in the gill during acclimation to seawater. This is the first report of the use of vivo-morpholinos in adult killifish and demonstrates that vivo-morpholinos are a valuable genetic tool for this environmentally relevant model organism

A Population Proportion approach for ranking differentially expressed genes

<p>Abstract</p> <p>Background</p> <p>DNA microarrays are used to investigate differences in gene expression between two or more classes of samples. Most currently used approaches compare mean expression levels between classes and are not geared to find genes whose expression is significantly different in only a subset of samples in a class. However, biological variability can lead to situations where key genes are differentially expressed in only a subset of samples. To facilitate the identification of such genes, a new method is reported.</p> <p>Methods</p> <p>The key difference between the Population Proportion Ranking Method (PPRM) presented here and almost all other methods currently used is in the quantification of variability. PPRM quantifies variability in terms of inter-sample ratios and can be used to calculate the relative merit of differentially expressed genes with a specified difference in expression level between at least some samples in the two classes, which at the same time have lower than a specified variability within each class.</p> <p>Results</p> <p>PPRM is tested on simulated data and on three publicly available cancer data sets. It is compared to the t test, PPST, COPA, OS, ORT and MOST using the simulated data. Under the conditions tested, it performs as well or better than the other methods tested under low intra-class variability and better than t test, PPST, COPA and OS when a gene is differentially expressed in only a subset of samples. It performs better than ORT and MOST in recognizing non differentially expressed genes with high variability in expression levels across all samples. For biological data, the success of predictor genes identified in appropriately classifying an independent sample is reported.</p

Minimization of Costs and Energy Consumption in a Data Center by a Workload-Based Capacity Management

International audienceIn this paper we present an approach to improve power and cooling capacity management in a data center by taking into account knowledge about applications and workloads. We apply power capping techniques and proper cooling infrastructure configuration to achieve savings in energy and costs. To estimate values of a total energy consumption and costs we simulate both IT software/hardware and cooling infrastructure at once using the CoolEmAll SVD Toolkit. We also investigated the use of power capping to adjust data center operation to variable power supply and pricing. By better adjusting cooling infrastructure to specific types of workloads, we were able to find a configuration which resulted in energy, OPEX and CAPEX savings in the range of 4–25 %