Data structures for SIMD logic simulation

Due to the growth of design size and complexity, design verification is an important aspect of the Logic Circuit development process. The purpose of verification is to validate that the design meets the system requirements and specification. This is done by either functional or formal verification. The most popular approach to functional verification is the use of simulation based techniques. Using models to replicate the behaviour of an actual system is called simulation. In this thesis, a software/data structure architecture without explicit locks is proposed to accelerate logic gate circuit simulation. We call thus system ZSIM. The ZSIM software architecture simulator targets low cost SIMD multi-core machines. Its performance is evaluated on the Intel Xeon Phi and 2 other machines (Intel Xeon and AMD Opteron). The aim of these experiments is to: • Verify that the data structure used allows SIMD acceleration, particularly on machines with gather instructions ( section 5.3.1). • Verify that, on sufficiently large circuits, substantial gains could be made from multicore parallelism ( section 5.3.2 ). • Show that a simulator using this approach out-performs an existing commercial simulator on a standard workstation ( section 5.3.3 ). • Show that the performance on a cheap Xeon Phi card is competitive with results reported elsewhere on much more expensive super-computers ( section 5.3.5 ). To evaluate the ZSIM, two types of test circuits were used: 1. Circuits from the IWLS benchmark suit [1] which allow direct comparison with other published studies of parallel simulators.2. Circuits generated by a parametrised circuit synthesizer. The synthesizer used an algorithm that has been shown to generate circuits that are statistically representative of real logic circuits. The synthesizer allowed testing of a range of very large circuits, larger than the ones for which it was possible to obtain open source files. The experimental results show that with SIMD acceleration and multicore, ZSIM gained a peak parallelisation factor of 300 on Intel Xeon Phi and 11 on Intel Xeon. With only SIMD enabled, ZSIM achieved a maximum parallelistion gain of 10 on Intel Xeon Phi and 4 on Intel Xeon. Furthermore, it was shown that this software architecture simulator running on a SIMD machine is much faster than, and can handle much bigger circuits than a widely used commercial simulator (Xilinx) running on a workstation. The performance achieved by ZSIM was also compared with similar pre-existing work on logic simulation targeting GPUs and supercomputers. It was shown that ZSIM simulator running on a Xeon Phi machine gives comparable simulation performance to the IBM Blue Gene supercomputer at very much lower cost. The experimental results have shown that the Xeon Phi is competitive with simulation on GPUs and allows the handling of much larger circuits than have been reported for GPU simulation. When targeting Xeon Phi architecture, the automatic cache management of the Xeon Phi, handles and manages the on-chip local store without any explicit mention of the local store being made in the architecture of the simulator itself. However, targeting GPUs, explicit cache management in program increases the complexity of the software architecture. Furthermore, one of the strongest points of the ZSIM simulator is its portability. Note that the same code was tested on both AMD and Xeon Phi machines. The same architecture that efficiently performs on Xeon Phi, was ported into a 64 core NUMA AMD Opteron. To conclude, the two main achievements are restated as following: The primary achievement of this work was proving that the ZSIM architecture was faster than previously published logic simulators on low cost platforms. The secondary achievement was the development of a synthetic testing suite that went beyond the scale range that was previously publicly available, based on prior work that showed the synthesis technique is valid.

Data warehouse para el Centro de Documentación Regional "Juan Bautista Vázquez"

El proceso de toma de decisiones en las bibliotecas universitarias es de suma importancia, sin embargo, se encuentra complicaciones como la gran cantidad de fuentes de datos y los grandes volúmenes de datos a analizar. Las bibliotecas universitarias están acostumbradas a producir y recopilar una gran cantidad de información sobre sus datos y servicios. Las fuentes de datos comunes son el resultado de sistemas internos, portales y catálogos en línea, evaluaciones de calidad y encuestas. Desafortunadamente estas fuentes de datos sólo se utilizan parcialmente para la toma de decisiones debido a la amplia variedad de formatos y estándares, así como la falta de métodos eficientes y herramientas de integración. Este proyecto de tesis presenta el análisis, diseño e implementación del Data Warehouse, que es un sistema integrado de toma de decisiones para el Centro de Documentación Juan Bautista Vázquez. En primer lugar se presenta los requerimientos y el análisis de los datos en base a una metodología, esta metodología incorpora elementos claves incluyendo el análisis de procesos, la calidad estimada, la información relevante y la interacción con el usuario que influyen en una decisión bibliotecaria. A continuación, se propone la arquitectura y el diseño del Data Warehouse y su respectiva implementación la misma que soporta la integración, procesamiento y el almacenamiento de datos. Finalmente los datos almacenados se analizan a través de herramientas de procesamiento analítico y la aplicación de técnicas de Bibliomining ayudando a los administradores del centro de documentación a tomar decisiones óptimas sobre sus recursos y servicios.

EMMA 2--a MAGE-compliant system for the collaborative analysis and integration of microarray data

Background: Understanding transcriptional regulation by genome-wide microarray studies can contribute to unravel complex relationships between genes. Attempts to standardize the annotation of microarray data include the Minimum Information About a Microarray Experiment (MIAME) recommendations, the MAGE-ML format for data interchange, and the use of controlled vocabularies or ontologies. The existing software systems for microarray data analysis implement the mentioned standards only partially and are often hard to use and extend. Integration of genomic annotation data and other sources of external knowledge using open standards is therefore a key requirement for future integrated analysis systems. Results: The EMMA 2 software has been designed to resolve shortcomings with respect to full MAGE-ML and ontology support and makes use of modern data integration techniques. We present a software system that features comprehensive data analysis functions for spotted arrays, and for the most common synthesized oligo arrays such as Agilent, Affymetrix and NimbleGen. The system is based on the full MAGE object model. Analysis functionality is based on R and Bioconductor packages and can make use of a compute cluster for distributed services. Conclusion: Our model-driven approach for automatically implementing a full MAGE object model provides high flexibility and compatibility. Data integration via SOAP-based web-services is advantageous in a distributed client-server environment as the collaborative analysis of microarray data is gaining more and more relevance in international research consortia. The adequacy of the EMMA 2 software design and implementation has been proven by its application in many distributed functional genomics projects. Its scalability makes the current architecture suited for extensions towards future transcriptomics methods based on high-throughput sequencing approaches which have much higher computational requirements than microarrays.

Using ICT Energy consumption for monitoring ICT architecture

Energy efficient policies are being applied to network protocols, devices and classical network management systems. Researchers have already studied in depth each of those fields, including for instance a long monitoring processes of various number of individual ICT equipment from where power models are constructed. With the development of smart meters and emerging protocols such as SNMP and NETCONF, currently there is an open field to couple the power models, translated to the expected behavior, with the realtime energy measurements. The goal is to derive a comparison on the power data between both of the processes in the direction of detection for possible deviations on the expected results. The logical assumption is that a fault in the usage of a particular device will not only increase its own energy usage, but also may cause additional consumption on the other devices part of the network. A platform is developed to monitor and analyze the retrieved power data of a simulated enterprise ICT infrastructure. Moreover, smart algorithms are developed which are aware of the different states that are occurring on each device during their typical use phase, as well as to detect and isolate possible anomalies. The produced results are obtained and validated with the use of Cisco switches and routers, Dell Precision stations and Raritan PDU as part of the monitored infrastructure.

Contributions to 3D Data Registration and Representation

Nowadays, new computers generation provides a high performance that enables to build computationally expensive computer vision applications applied to mobile robotics. Building a map of the environment is a common task of a robot and is an essential part to allow the robots to move through these environments. Traditionally, mobile robots used a combination of several sensors from different technologies. Lasers, sonars and contact sensors have been typically used in any mobile robotic architecture, however color cameras are an important sensor due to we want the robots to use the same information that humans to sense and move through the different environments. Color cameras are cheap and flexible but a lot of work need to be done to give robots enough visual understanding of the scenes. Computer vision algorithms are computational complex problems but nowadays robots have access to different and powerful architectures that can be used for mobile robotics purposes. The advent of low-cost RGB-D sensors like Microsoft Kinect which provide 3D colored point clouds at high frame rates made the computer vision even more relevant in the mobile robotics field. The combination of visual and 3D data allows the systems to use both computer vision and 3D processing and therefore to be aware of more details of the surrounding environment. The research described in this thesis was motivated by the need of scene mapping. Being aware of the surrounding environment is a key feature in many mobile robotics applications from simple robotic navigation to complex surveillance applications. In addition, the acquisition of a 3D model of the scenes is useful in many areas as video games scene modeling where well-known places are reconstructed and added to game systems or advertising where once you get the 3D model of one room the system can add furniture pieces using augmented reality techniques. In this thesis we perform an experimental study of the state-of-the-art registration methods to find which one fits better to our scene mapping purposes. Different methods are tested and analyzed on different scene distributions of visual and geometry appearance. In addition, this thesis proposes two methods for 3d data compression and representation of 3D maps. Our 3D representation proposal is based on the use of Growing Neural Gas (GNG) method. This Self-Organizing Maps (SOMs) has been successfully used for clustering, pattern recognition and topology representation of various kind of data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models without considering time constraints. Self-organising neural models have the ability to provide a good representation of the input space. In particular, the Growing Neural Gas (GNG) is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation. However, this type of learning is time consuming, specially for high-dimensional input data. Since real applications often work under time constraints, it is necessary to adapt the learning process in order to complete it in a predefined time. This thesis proposes a hardware implementation leveraging the computing power of modern GPUs which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). Our proposed geometrical 3D compression method seeks to reduce the 3D information using plane detection as basic structure to compress the data. This is due to our target environments are man-made and therefore there are a lot of points that belong to a plane surface. Our proposed method is able to get good compression results in those man-made scenarios. The detected and compressed planes can be also used in other applications as surface reconstruction or plane-based registration algorithms. Finally, we have also demonstrated the goodness of the GPU technologies getting a high performance implementation of a CAD/CAM common technique called Virtual Digitizing.

Energy Efficiency of Software Transactional Memory in a Heterogeneous Architecture

Hardware vendors make an important effort creating low-power CPUs that keep battery duration and durability above acceptable levels. In order to achieve this goal and provide good performance-energy for a wide variety of applications, ARM designed the big.LITTLE architecture. This heterogeneous multi-core architecture features two different types of cores: big cores oriented to performance and little cores, slower and aimed to save energy consumption. As all the cores have access to the same memory, multi-threaded applications must resort to some mutual exclusion mechanism to coordinate the access to shared data by the concurrent threads. Transactional Memory (TM) represents an optimistic approach for shared-memory synchronization. To take full advantage of the features offered by software TM, but also benefit from the characteristics of the heterogeneous big.LITTLE architectures, our focus is to propose TM solutions that take into account the power/performance requirements of the application and what it is offered by the architecture. In order to understand the current state-of-the-art and obtain useful information for future power-aware software TM solutions, we have performed an analysis of a popular TM library running on top of an ARM big.LITTLE processor. Experiments show, in general, better scalability for the LITTLE cores for most of the applications except for one, which requires the computing performance that the big cores offer.

MINING AND VERIFICATION OF TEMPORAL EVENTS WITH APPLICATIONS IN COMPUTER MICRO-ARCHITECTURE RESEARCH

Computer simulation programs are essential tools for scientists and engineers to understand a particular system of interest. As expected, the complexity of the software increases with the depth of the model used. In addition to the exigent demands of software engineering, verification of simulation programs is especially challenging because the models represented are complex and ridden with unknowns that will be discovered by developers in an iterative process. To manage such complexity, advanced verification techniques for continually matching the intended model to the implemented model are necessary. Therefore, the main goal of this research work is to design a useful verification and validation framework that is able to identify model representation errors and is applicable to generic simulators. The framework that was developed and implemented consists of two parts. The first part is First-Order Logic Constraint Specification Language (FOLCSL) that enables users to specify the invariants of a model under consideration. From the first-order logic specification, the FOLCSL translator automatically synthesizes a verification program that reads the event trace generated by a simulator and signals whether all invariants are respected. The second part consists of mining the temporal flow of events using a newly developed representation called State Flow Temporal Analysis Graph (SFTAG). While the first part seeks an assurance of implementation correctness by checking that the model invariants hold, the second part derives an extended model of the implementation and hence enables a deeper understanding of what was implemented. The main application studied in this work is the validation of the timing behavior of micro-architecture simulators. The study includes SFTAGs generated for a wide set of benchmark programs and their analysis using several artificial intelligence algorithms. This work improves the computer architecture research and verification processes as shown by the case studies and experiments that have been conducted.

Host and Network Optimizations for Performance Enhancement and Energy Efficiency in Data Center Networks

Modern data centers host hundreds of thousands of servers to achieve economies of scale. Such a huge number of servers create challenges for the data center network (DCN) to provide proportionally large bandwidth. In addition, the deployment of virtual machines (VMs) in data centers raises the requirements for efficient resource allocation and find-grained resource sharing. Further, the large number of servers and switches in the data center consume significant amounts of energy. Even though servers become more energy efficient with various energy saving techniques, DCN still accounts for 20% to 50% of the energy consumed by the entire data center. The objective of this dissertation is to enhance DCN performance as well as its energy efficiency by conducting optimizations on both host and network sides. First, as the DCN demands huge bisection bandwidth to interconnect all the servers, we propose a parallel packet switch (PPS) architecture that directly processes variable length packets without segmentation-and-reassembly (SAR). The proposed PPS achieves large bandwidth by combining switching capacities of multiple fabrics, and it further improves the switch throughput by avoiding padding bits in SAR. Second, since certain resource demands of the VM are bursty and demonstrate stochastic nature, to satisfy both deterministic and stochastic demands in VM placement, we propose the Max-Min Multidimensional Stochastic Bin Packing (M3SBP) algorithm. M3SBP calculates an equivalent deterministic value for the stochastic demands, and maximizes the minimum resource utilization ratio of each server. Third, to provide necessary traffic isolation for VMs that share the same physical network adapter, we propose the Flow-level Bandwidth Provisioning (FBP) algorithm. By reducing the flow scheduling problem to multiple stages of packet queuing problems, FBP guarantees the provisioned bandwidth and delay performance for each flow. Finally, while DCNs are typically provisioned with full bisection bandwidth, DCN traffic demonstrates fluctuating patterns, we propose a joint host-network optimization scheme to enhance the energy efficiency of DCNs during off-peak traffic hours. The proposed scheme utilizes a unified representation method that converts the VM placement problem to a routing problem and employs depth-first and best-fit search to find efficient paths for flows.

Improving Resource Management in Virtualized Data Centers using Application Performance Models

The rapid growth of virtualized data centers and cloud hosting services is making the management of physical resources such as CPU, memory, and I/O bandwidth in data center servers increasingly important. Server management now involves dealing with multiple dissimilar applications with varying Service-Level-Agreements (SLAs) and multiple resource dimensions. The multiplicity and diversity of resources and applications are rendering administrative tasks more complex and challenging. This thesis aimed to develop a framework and techniques that would help substantially reduce data center management complexity. We specifically addressed two crucial data center operations. First, we precisely estimated capacity requirements of client virtual machines (VMs) while renting server space in cloud environment. Second, we proposed a systematic process to efficiently allocate physical resources to hosted VMs in a data center. To realize these dual objectives, accurately capturing the effects of resource allocations on application performance is vital. The benefits of accurate application performance modeling are multifold. Cloud users can size their VMs appropriately and pay only for the resources that they need; service providers can also offer a new charging model based on the VMs performance instead of their configured sizes. As a result, clients will pay exactly for the performance they are actually experiencing; on the other hand, administrators will be able to maximize their total revenue by utilizing application performance models and SLAs. This thesis made the following contributions. First, we identified resource control parameters crucial for distributing physical resources and characterizing contention for virtualized applications in a shared hosting environment. Second, we explored several modeling techniques and confirmed the suitability of two machine learning tools, Artificial Neural Network and Support Vector Machine, to accurately model the performance of virtualized applications. Moreover, we suggested and evaluated modeling optimizations necessary to improve prediction accuracy when using these modeling tools. Third, we presented an approach to optimal VM sizing by employing the performance models we created. Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center.

Exploring Hidden Coherent Feature Groups and Temporal Semantics for Multimedia Big Data Analysis

Thanks to the advanced technologies and social networks that allow the data to be widely shared among the Internet, there is an explosion of pervasive multimedia data, generating high demands of multimedia services and applications in various areas for people to easily access and manage multimedia data. Towards such demands, multimedia big data analysis has become an emerging hot topic in both industry and academia, which ranges from basic infrastructure, management, search, and mining to security, privacy, and applications. Within the scope of this dissertation, a multimedia big data analysis framework is proposed for semantic information management and retrieval with a focus on rare event detection in videos. The proposed framework is able to explore hidden semantic feature groups in multimedia data and incorporate temporal semantics, especially for video event detection. First, a hierarchical semantic data representation is presented to alleviate the semantic gap issue, and the Hidden Coherent Feature Group (HCFG) analysis method is proposed to capture the correlation between features and separate the original feature set into semantic groups, seamlessly integrating multimedia data in multiple modalities. Next, an Importance Factor based Temporal Multiple Correspondence Analysis (i.e., IF-TMCA) approach is presented for effective event detection. Specifically, the HCFG algorithm is integrated with the Hierarchical Information Gain Analysis (HIGA) method to generate the Importance Factor (IF) for producing the initial detection results. Then, the TMCA algorithm is proposed to efficiently incorporate temporal semantics for re-ranking and improving the final performance. At last, a sampling-based ensemble learning mechanism is applied to further accommodate the imbalanced datasets. In addition to the multimedia semantic representation and class imbalance problems, lack of organization is another critical issue for multimedia big data analysis. In this framework, an affinity propagation-based summarization method is also proposed to transform the unorganized data into a better structure with clean and well-organized information. The whole framework has been thoroughly evaluated across multiple domains, such as soccer goal event detection and disaster information management.

Enabling effective operational decision making on a Combined Heat and Power System using the 5C architecture

The use of Cyber Physical Systems (CPS) to optimise industrial energy systems is an approach which has the potential to positively impact on manufacturing sector energy efficiency. The need to obtain data to facilitate the implementation of a CPS in an industrial energy system is however a complex task which is often implemented in a non-standardised way. The use of the 5C CPS architecture has the potential to standardise this approach. This paper describes a case study where data from a Combined Heat and Power (CHP) system located in a large manufacturing company was fused with grid electricity and gas models as well as a maintenance cost model using the 5C architecture with a view to making effective decisions on its cost efficient operation. A control change implemented based on the cognitive analysis enabled via the 5C architecture implementation has resulted in energy cost savings of over €7400 over a four-month period, with energy cost savings of over €150,000 projected once the 5C architecture is extended into the production environment.

Measuring QoS in an aeronautical opportunistic network architecture with limited access to a satellite communications backhaul

We measure quality of service (QoS) in a wireless network architecture of transoceanic aircraft. A distinguishing characteristic of the network scheme we analyze is that it mixes the concept of Delay Tolerant Networking (DTN) through the exploitation of opportunistic contacts, together with direct satellite access in a limited number of the nodes. We provide a graph sparsification technique for deriving a network model that satisfies the key properties of a real aeronautical opportunistic network while enabling scalable simulation. This reduced model allows us to analyze the impact regarding QoS of introducing Internet-like traffic in the form of outgoing data from passengers. Promoting QoS in DTNs is usually really challenging due to their long delays and scarce resources. The availability of satellite communication links offers a chance to provide an improved degree of service regarding a pure opportunistic approach, and therefore it needs to be properly measured and quantified. Our analysis focuses on several QoS indicators such as delivery time, delivery ratio, and bandwidth allocation fairness. Obtained results show significant improvements in all metric indicators regarding QoS, not usually achievable on the field of DTNs.

M.A.S. [MADRID ARCHITECTURE SEOUL] Exhibition Pavilion

A metacity represented by architectural clouds. Architecture and public spaces, represented symbolically and graphically on the ground plane.

Development of JADE, a new software for the verification and validation of nuclear data libraries

Nuclear cross sections are the pillars onto which the transport simulation of particles and radiations is built on. Since the nuclear data libraries production chain is extremely complex and made of different steps, it is mandatory to foresee stringent verification and validation procedures to be applied to it. The work here presented has been focused on the development of a new python based software called JADE, whose objective is to give a significant help in increasing the level of automation and standardization of these procedures in order to reduce the time passing between new libraries releases and, at the same time, increasing their quality. After an introduction to nuclear fusion (which is the field where the majority of the V\&V action was concentrated for the time being) and to the simulation of particles and radiations transport, the motivations leading to JADE development are discussed. Subsequently, the code general architecture and the implemented benchmarks (both experimental and computational) are described. After that, the results coming from the major application of JADE during the research years are presented. At last, after a final discussion on the objective reached by JADE, the possible brief, mid and long time developments for the project are discussed.

Decentralized systems for the protection and portability of personal data

The General Data Protection Regulation (GDPR) has been designed to help promote a view in favor of the interests of individuals instead of large corporations. However, there is the need of more dedicated technologies that can help companies comply with GDPR while enabling people to exercise their rights. We argue that such a dedicated solution must address two main issues: the need for more transparency towards individuals regarding the management of their personal information and their often hindered ability to access and make interoperable personal data in a way that the exercise of one's rights would result in straightforward. We aim to provide a system that helps to push personal data management towards the individual's control, i.e., a personal information management system (PIMS). By using distributed storage and decentralized computing networks to control online services, users' personal information could be shifted towards those directly concerned, i.e., the data subjects. The use of Distributed Ledger Technologies (DLTs) and Decentralized File Storage (DFS) as an implementation of decentralized systems is of paramount importance in this case. The structure of this dissertation follows an incremental approach to describing a set of decentralized systems and models that revolves around personal data and their subjects. Each chapter of this dissertation builds up the previous one and discusses the technical implementation of a system and its relation with the corresponding regulations. We refer to the EU regulatory framework, including GDPR, eIDAS, and Data Governance Act, to build our final system architecture's functional and non-functional drivers. In our PIMS design, personal data is kept in a Personal Data Space (PDS) consisting of encrypted personal data referring to the subject stored in a DFS. On top of that, a network of authorization servers acts as a data intermediary to provide access to potential data recipients through smart contracts.