Data Science and Ebola

Data Science---Today, everybody and everything produces data. People produce large amounts of data in social networks and in commercial transactions. Medical, corporate, and government databases continue to grow. Sensors continue to get cheaper and are increasingly connected, creating an Internet of Things, and generating even more data. In every discipline, large, diverse, and rich data sets are emerging, from astrophysics, to the life sciences, to the behavioral sciences, to finance and commerce, to the humanities and to the arts. In every discipline people want to organize, analyze, optimize and understand their data to answer questions and to deepen insights. The science that is transforming this ocean of data into a sea of knowledge is called data science. This lecture will discuss how data science has changed the way in which one of the most visible challenges to public health is handled, the 2014 Ebola outbreak in West Africa.Comment: Inaugural lecture Leiden Universit

Energy Concerns with HPC Systems and Applications

For various reasons including those related to climate changes, {\em energy} has become a critical concern in all relevant activities and technical designs. For the specific case of computer activities, the problem is exacerbated with the emergence and pervasiveness of the so called {\em intelligent devices}. From the application side, we point out the special topic of {\em Artificial Intelligence}, who clearly needs an efficient computing support in order to succeed in its purpose of being a {\em ubiquitous assistant}. There are mainly two contexts where {\em energy} is one of the top priority concerns: {\em embedded computing} and {\em supercomputing}. For the former, power consumption is critical because the amount of energy that is available for the devices is limited. For the latter, the heat dissipated is a serious source of failure and the financial cost related to energy is likely to be a significant part of the maintenance budget. On a single computer, the problem is commonly considered through the electrical power consumption. This paper, written in the form of a survey, we depict the landscape of energy concerns in computer activities, both from the hardware and the software standpoints.Comment: 20 page

Review and Analysis of Failure Detection and Prevention Techniques in IT Infrastructure Monitoring

Maintaining the health of IT infrastructure components for improved reliability and availability is a research and innovation topic for many years. Identification and handling of failures are crucial and challenging due to the complexity of IT infrastructure. System logs are the primary source of information to diagnose and fix failures. In this work, we address three essential research dimensions about failures, such as the need for failure handling in IT infrastructure, understanding the contribution of system-generated log in failure detection and reactive & proactive approaches used to deal with failure situations. This study performs a comprehensive analysis of existing literature by considering three prominent aspects as log preprocessing, anomaly & failure detection, and failure prevention. With this coherent review, we (1) presume the need for IT infrastructure monitoring to avoid downtime, (2) examine the three types of approaches for anomaly and failure detection such as a rule-based, correlation method and classification, and (3) fabricate the recommendations for researchers on further research guidelines. As far as the authors\u27 knowledge, this is the first comprehensive literature review on IT infrastructure monitoring techniques. The review has been conducted with the help of meta-analysis and comparative study of machine learning and deep learning techniques. This work aims to outline significant research gaps in the area of IT infrastructure failure detection. This work will help future researchers understand the advantages and limitations of current methods and select an adequate approach to their problem

HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges

High Performance Computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost-benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show hybrid environments are the natural path to get the best of the on-premise and cloud resources---steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This paper brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR

Sustainable HPC: Modeling, Characterization, and Implications of Carbon Footprint in Modern HPC Systems

The rapid growth in demand for HPC systems has led to a rise in energy consumption and carbon emissions, which requires urgent intervention. In this work, we present a comprehensive framework for analyzing the carbon footprint of high-performance computing (HPC) systems, considering the carbon footprint during both the hardware production and system operational stages. Our work employs HPC hardware component carbon footprint modeling, regional carbon intensity analysis, and experimental characterization of the system life cycle to highlight the importance of quantifying the carbon footprint of an HPC system holistically

Soft Computing Techiniques for the Protein Folding Problem on High Performance Computing Architectures

The protein-folding problem has been extensively studied during the last fifty years. The understanding of the dynamics of global shape of a protein and the influence on its biological function can help us to discover new and more effective drugs to deal with diseases of pharmacological relevance. Different computational approaches have been developed by different researchers in order to foresee the threedimensional arrangement of atoms of proteins from their sequences. However, the computational complexity of this problem makes mandatory the search for new models, novel algorithmic strategies and hardware platforms that provide solutions in a reasonable time frame. We present in this revision work the past and last tendencies regarding protein folding simulations from both perspectives; hardware and software. Of particular interest to us are both the use of inexact solutions to this computationally hard problem as well as which hardware platforms have been used for running this kind of Soft Computing techniques.This work is jointly supported by the FundaciónSéneca (Agencia Regional de Ciencia y Tecnología, Región de Murcia) under grants 15290/PI/2010 and 18946/JLI/13, by the Spanish MEC and European Commission FEDER under grant with reference TEC2012-37945-C02-02 and TIN2012-31345, by the Nils Coordinated Mobility under grant 012-ABEL-CM-2014A, in part financed by the European Regional Development Fund (ERDF). We also thank NVIDIA for hardware donation within UCAM GPU educational and research centers.Ingeniería, Industria y Construcció