We present a proof of concept implementation of the in-memory computing paradigm that we use to facilitate the analysis of metagenomic sequencing reads. In doing so we compare the performance of POSIX™file systems and key-value storage for omics data, and we show the potential for integrating high-performance computing (HPC) and cloud native technologies. We show that in-memory key-value storage offers possibilities for improved handling of omics data through more flexible and faster data processing. We envision fully containerized workflows and their deployment in portable micro-pipelines with multiple instances working concurrently with the same distributed in-memory storage. To highlight the potential usage of this technology for event driven and real-time data processing, we use a biological case study focused on the growing threat of antimicrobial resistance (AMR). We develop a workflow encompassing bioinformatics and explainable machine learning (ML) to predict life expectancy of a population based on the microbiome of its sewage while providing a description of AMR contribution to the prediction. We propose that in future, performing such analyses in 'real-time' would allow us to assess the potential risk to the population based on changes in the AMR profile of the community.
Abstract We present bespoke energy efficiency optimizations in high performance computing (HPC) environments using holistic approach to data collection, analysis and proactive management of resources and workloads. Our solution has three major components: (i) platform for collecting, storing and processing data from multiple sources across hardware and software stacks, (ii) collections of regression machine learning (ML) algorithms for making workloads classifications and energy usage predictions, (iii) agent‐based decision‐making framework for delivering control decisions to middle‐ware and infrastructure thus supporting real time or near real energy efficiency optimizations. We will provide some concrete examples of using our proposed approach in HPC environment.
Power consumption of the world's leading supercomputers is of the order of tens of MegaWatts (MW). Therefore, energy efficiency and power management of High Performance Computing (HPC) systems are among the main goals of the HPC community. This paper presents our study of managing energy consumption of supercomputers with the use of the energy aware workload management software IBM Platform Load Sharing Facility (LSF). We analyze energy consumption and workloads of the IBM NextScale Cluster, BlueWonder, located at the Daresbury Laboratory, STFC, UK. We describe power management algorithms implemented as Energy Aware Scheduling (EAS) policies in the IBM Platform LSF software. We show the effect of the power management policies on supercomputer efficiency and power consumption using experimental as well as simulated data from scientific workloads on the BlueWonder supercomputer. We observed energy saving of up to 12% from EAS policies.
Cytometry of Reaction Rate Constant (CRRC) is a method for studying heterogeneity of cell populations with regards to activity of cellular reactions. It is based on time-lapse fluorescence microscopy which facilitates following reaction kinetics in individual cells. The current CRRC workflow utilizes a single fluorescence image to manually identify cell contours; these contours are then used to determine fluorescence intensity of individual cells in the entire time-stack of images. This workflow can only be used reliably if the cells maintain their positions during the time-lapse measurements; if the cells move, the results of a CRRC experiment will be inaccurate. The requirement of invariant cell positions during a prolonged imaging is impossible to satisfy for motile cells. Here we report on developing an advanced workflow that makes CRRC applicable to motile cells. The new workflow combines fluorescence microscopy with brightfield (BF) microscopy and utilizes automated processing and analysis of images. A BF image is taken right after every fluorescence image and used to determine cell contours. The contours are tracked through the time-stack of BF images to account for cell movement. A set of contours, which is unique for every image, is then used to determine fluorescence intensity of cells in the associated fluorescent image. Finally, time dependencies of intracellular fluorescence intensities are used to determine the rate constant and plot a kinetic histogram “number of cells vs rate constant”. The robustness of the new workflow to cell movement was confirmed experimentally by conducting a CRRC study of cross-membrane transport in motile cells. The new workflow makes CRRC applicable to a wide range of cell types and eliminates the influence of cell motility on the accuracy of results.
Numerical and theoretical studies of laser beam interaction with underdense plasmas involving ion wave instabilities are presented. The theoretical model that is used involves realistic distribution of laser intensity in a focal spot and a non-paraxial electromagnetic wave equation coupled to the ion acoustic wave equation in a two-dimensional geometry. Included among the important results is a weak correlation between backscattered stimulated Brillouin scattering (SBS) reflectivity and filamentation or self-focusing instabilities. The transmitted light demonstrates angular spreading and frequency shifts consistent with near-forward SBS. The role of filamentation and self-focusing on the transmitted light is also discussed.
Abstract Low convergence ratio implosions (where wetted-foam layers are used to limit capsule convergence, achieving improved robustness to instability growth) and auxiliary heating (where electron beams are used to provide collisionless heating of a hotspot) are two promising techniques that are being explored for inertial fusion energy applications. In this paper, a new analytic study is presented to understand and predict the performance of these implosions. Firstly, conventional gain models are adapted to produce gain curves for fixed convergence ratios, which are shown to well-describe previously simulated results. Secondly, auxiliary heating is demonstrated to be well understood and interpreted through the burn-up fraction of the deuterium-tritium fuel, with the gradient of burn-up with respect to burn-averaged temperature shown to provide good qualitative predictions of the effectiveness of this technique for a given implosion. Simulations of auxiliary heating for a range of implosions are presented in support of this and demonstrate that this heating can have significant benefit for high gain implosions, being most effective when the burn-averaged temperature is between 5 and 20 keV.
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