SInCom 2015

2nd Baden-Württemberg Center of Applied Research Symposium on Information and Communication Systems, SInCom 2015, 13. November 2015 in Konstan

Development of an oceanographic application in HPC

High Performance Computing (HPC) is used for running advanced application programs efficiently, reliably, and quickly. In earlier decades, performance analysis of HPC applications was evaluated based on speed, scalability of threads, memory hierarchy. Now, it is essential to consider the energy or the power consumed by the system while executing an application. In fact, the High Power Consumption (HPC) is one of biggest problems for the High Performance Computing (HPC) community and one of the major obstacles for exascale systems design. The new generations of HPC systems intend to achieve exaflop performances and will demand even more energy to processing and cooling. Nowadays, the growth of HPC systems is limited by energy issues Recently, many research centers have focused the attention on doing an automatic tuning of HPC applications which require a wide study of HPC applications in terms of power efficiency. In this context, this paper aims to propose the study of an oceanographic application, named OceanVar, that implements Domain Decomposition based 4D Variational model (DD-4DVar), one of the most commonly used HPC applications, going to evaluate not only the classic aspects of performance but also aspects related to power efficiency in different case of studies. These work were realized at Bsc (Barcelona Supercomputing Center), Spain within the Mont-Blanc project, performing the test first on HCA server with Intel technology and then on a mini-cluster Thunder with ARM technology. In this work of thesis it was initially explained the concept of assimilation date, the context in which it is developed, and a brief description of the mathematical model 4DVAR. After this problem’s close examination, it was performed a porting from Matlab description of the problem of data-assimilation to its sequential version in C language. Secondly, after identifying the most onerous computational kernels in order of time, it has been developed a parallel version of the application with a parallel multiprocessor programming style, using the MPI (Message Passing Interface) protocol. The experiments results, in terms of performance, have shown that, in the case of running on HCA server, an Intel architecture, values of efficiency of the two most onerous functions obtained, growing the number of process, are approximately equal to 80%. In the case of running on ARM architecture, specifically on Thunder mini-cluster, instead, the trend obtained is labeled as "SuperLinear Speedup" and, in our case, it can be explained by a more efficient use of resources (cache memory access) compared with the sequential case. In the second part of this paper was presented an analysis of the some issues of this application that has impact in the energy efficiency. After a brief discussion about the energy consumption characteristics of the Thunder chip in technological landscape, through the use of a power consumption detector, the Yokogawa Power Meter, values of energy consumption of mini-cluster Thunder were evaluated in order to determine an overview on the power-to-solution of this application to use as the basic standard for successive analysis with other parallel styles. Finally, a comprehensive performance evaluation, targeted to estimate the goodness of MPI parallelization, is conducted using a suitable performance tool named Paraver, developed by BSC. Paraver is such a performance analysis and visualisation tool which can be used to analyse MPI, threaded or mixed mode programmes and represents the key to perform a parallel profiling and to optimise the code for High Performance Computing. A set of graphical representation of these statistics make it easy for a developer to identify performance problems. Some of the problems that can be easily identified are load imbalanced decompositions, excessive communication overheads and poor average floating operations per second achieved. Paraver can also report statistics based on hardware counters, which are provided by the underlying hardware. This project aimed to use Paraver configuration files to allow certain metrics to be analysed for this application. To explain in some way the performance trend obtained in the case of analysis on the mini-cluster Thunder, the tracks were extracted from various case of studies and the results achieved is what expected, that is a drastic drop of cache misses by the case ppn (process per node) = 1 to case ppn = 16. This in some way explains a more efficient use of cluster resources with an increase of the number of processes

An Embedded Biometric Sensor for Ubiquitous Authentication

Communication networks and distributed technologies move people towards the era of ubiquitous computing. An ubiquitous environment needs many authentication sensors for users recognition, in order to provide a secure infrastructure for both user access to resources and services and information management. Today the security requirements must ensure secure and trusted user information to protect sensitive data resource access and they could be used for user traceability inside the platform. Conventional authentication systems, based on username and password, are in crisis since they are not able to guarantee a suitable security level for several applications. Biometric authentication systems represent a valid alternative to the conventional authentication systems providing a flexible einfrastructure towards an integrated solution supporting the requirement for improved inter-organizational functionality. In this work the study and the implementation of a fingerprintsbased embedded biometric system is proposed. Typical strategies implemented in Identity Management Systems could be useful to protect biometric information. The proposed sensor can be seen as a self-contained sensor: it performs the all elaboration steps on board, a necessary requisite to strengthen security, so that sensible data are securely managed and stored inside the sensor, without any data leaking out. The sensor has been prototyped via an FPGA-based platform achieving fast execution time and a good final throughput. Resources used, elaboration times of the sensor are reported. Finally, recognition rates of the proposed embedded biometric sensor have been evaluated considering three different databases: the FVC2002 reference database, the CSAI/Biometrika proprietary database, and the CSAI/Secugen proprietary database. The best achieved FAR and FRR indexes are respectively 1.07% and 8.33%, with an elaboration time of 183.32 ms and a working frequency of 22.5 MHz

A Sorting Hat For Clusters. Dynamic Provisioning of Compute Nodes for Colocated Large Scale Computational Research Infrastructures

Current large scale computational research infrastructures are composed of multitudes of compute nodes fitted with similar or identical hardware. For practical purposes, the deployment of the software operating environment to each compute node is done in an automated fashion. If a data centre hosts more than one of these systems – for example cloud and HPC clusters – it is beneficial to use the same provisioning method for all of them. The uniform provisioning approach unifies administration of the various systems and allows flexible dedication and reconfiguration of computational resources. In particular, we will highlight the requirements on the underlying network infrastructure for unified remote boot but segregated service operations. Building upon this, we will present the Boot Selection Service, allowing for the addition, removal or rededication of a node to a given research infrastructure with a simple reconfiguration

Predictable migration and communication in the Quest-V multikernal

Quest-V is a system we have been developing from the ground up, with objectives focusing on safety, predictability and efficiency. It is designed to work on emerging multicore processors with hardware virtualization support. Quest-V is implemented as a ``distributed system on a chip'' and comprises multiple sandbox kernels. Sandbox kernels are isolated from one another in separate regions of physical memory, having access to a subset of processing cores and I/O devices. This partitioning prevents system failures in one sandbox affecting the operation of other sandboxes. Shared memory channels managed by system monitors enable inter-sandbox communication. The distributed nature of Quest-V means each sandbox has a separate physical clock, with all event timings being managed by per-core local timers. Each sandbox is responsible for its own scheduling and I/O management, without requiring intervention of a hypervisor. In this paper, we formulate bounds on inter-sandbox communication in the absence of a global scheduler or global system clock. We also describe how address space migration between sandboxes can be guaranteed without violating service constraints. Experimental results on a working system show the conditions under which Quest-V performs real-time communication and migration.National Science Foundation (1117025

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems