BackgroundMetagenomics is limited in its ability to link distinct microbial populations to genetic potential due to a current lack of representative isolate genome sequences. Reference-independent approaches, which exploit for example inherent genomic signatures for the clustering of metagenomic fragments (binning), offer the prospect to resolve and reconstruct population-level genomic complements without the need for prior knowledge.ResultsWe present VizBin, a Java™-based application which offers efficient and intuitive reference-independent visualization of metagenomic datasets from single samples for subsequent human-in-the-loop inspection and binning. The method is based on nonlinear dimension reduction of genomic signatures and exploits the superior pattern recognition capabilities of the human eye-brain system for cluster identification and delineation. We demonstrate the general applicability of VizBin for the analysis of metagenomic sequence data by presenting results from two cellulolytic microbial communities and one human-borne microbial consortium. The superior performance of our application compared to other analogous metagenomic visualization and binning methods is also presented.ConclusionsVizBin can be applied de novo for the visualization and subsequent binning of metagenomic datasets from single samples, and it can be used for the post hoc inspection and refinement of automatically generated bins. Due to its computational efficiency, it can be run on common desktop machines and enables the analysis of complex metagenomic datasets in a matter of minutes. The software implementation is available at https://claczny.github.io/VizBin under the BSD License (four-clause) and runs under Microsoft Windows™, Apple Mac OS X™ (10.7 to 10.10), and Linux.Electronic supplementary materialThe online version of this article (doi:10.1186/s40168-014-0066-1) contains supplementary material, which is available to authorized users.

SUMMARYVirtualization is emerging as the prominent approach to mutualise the energy consumed by a single server running multiple Virtual Machines (VMs) instances. The efficient utilization of virtualized servers and/or computing resources requires understanding of the overheads in energy consumption and the throughput, especially on high-demanding High Performance Computing (HPC) platforms. In this paper, a novel holistic model for the power of virtualized computing nodes is proposed. Moreover, we create and validate instances of the proposed model using concrete measures taken during a benchmarking process that reflects an HPC usage, i.e. HPCC, IOZone and Bonnie++, conducted using two different hardware configurations on Grid5000 platform, based on Intel and AMD processors, and three widespread virtualization frameworks, namely Xen, KVM, and VMware ESXi. The proposed holistic model of machine power takes into account the impact of utilisation metrics of the machine's components, as well as the employed application, virtualization, and hardware. The model is further derived using tools such as multiple linear regressions or neural networks that prove its elasticity, applicability and accuracy. The purpose of the model is to enable the estimation of energy consumption of virtualized platforms, aiming to make possible the optimization, scheduling or accounting in such systems, or their simulation.

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With a growing concern on the considerable energy consumed by HPC platforms and data centers, research efforts are targeting green approaches with higher energy efficiency. In particular, virtualization is emerging as the prominent approach to mutualize the energy consumed by a single server running multiple VMs instances. Even today, it remains unclear whether the overhead induced by virtualization and the corresponding hypervisor middleware suits an environment as high-demanding as an HPC platform. In this paper, we analyze from an HPC perspective the three most widespread virtualization frameworks, namely Xen, KVM, and VMware ESXi and compare them with a baseline environment running in native mode. We performed our experiments on the Grid'5000 platform by measuring the results of the reference HPL benchmark. Power measures were also performed in parallel to quantify the potential energy efficiency of the virtualized environments. In general, our study offers novel incentives toward in-house HPC platforms running without any virtualized frameworks.

SUMMARYVirtualization is emerging as the prominent approach to mutualise the energy consumed by a single server running multiple virtual machines instances. The efficient utilisation of virtualized servers and/or computing resources requires understanding of the overheads in energy consumption and the throughput especially on high‐demanding high‐performance computing (HPC) platforms. In this paper, a novel holistic model for the power of virtualized computing nodes is proposed. Moreover, we create and validate instances of the proposed model using concrete measures taken during a benchmarking process that reflects an HPC usage, that is, HPC challenge, IOZone and Bonnie++, conducted using two different hardware configurations on Grid'5000 platform, based on Intel and Advanced Micro Devices (AMD) processors and three widespread virtualization frameworks, namely, Xen, Kernel‐based virtual machine and VMware ESXi. The proposed holistic model of machine power takes into account the impact of utilisation metrics of the machine's components, as well as the employed application, virtualization and hardware. The model is further derived using tools such as multiple linear regressions or neural networks that prove its elasticity, applicability and accuracy. The purpose of the model is to enable the estimation of energy consumption of virtualized platforms, aiming to make possible the optimization, scheduling or accounting in such systems or their simulation. Copyright © 2014 John Wiley & Sons, Ltd.