Mesh traversal and sorting for efficient memory usage in scientific codes

Applications that operate on meshes are very popular in High Performance Computing (HPC) environments. In the past, many techniques have been developed in order to optimize the memory accesses for these datasets. Different loop transformations and domain decompositions are com- monly used for structured meshes. However, unstructured grids are more challenging. The memory accesses, based on the mesh connectivity, do not map well to the usual lin- ear memory model. This work presents a method to improve the memory performance which is suitable for HPC codes that operate on meshes. We develop a method to adjust the sequence in which the data are used inside the algorithm, by means of traversing and sorting the mesh. This sorted mesh can be transferred sequentially to the lower memory levels and allows for minimum data transfer requirements. The method also reduces the lower memory requirements dra- matically: up to 63% of the L1 cache misses are removed in a traditional cache system. We have obtained speedups of up to 2.58 on memory operations as measured in a general- purpose CPU. An improvement is also observed with se- quential access memories, where we have observed reduc- tions of up to 99% in the required low-level memory size.

GovCloud: the usage of cloud computing in public organizations

It has been shown that cloud computing brings cost benefits and promotes efficiency in the operations of the organizations, no matter what their type or size. However, few public organizations are benefiting from this new paradigm shift in the way the organizations consume and manage computational resources. The objective of this thesis is to analyze both internal and external factors that may influence the adoption of cloud computing by public organizations and propose possible strategies that can assist these organizations in their path to cloud usage. In order to achieve this objective, a SWOT analysis has been conducted, detecting internal factors (strengths and weaknesses) and external factors (opportunities and threats) that can influence the adoption of a governmental cloud. With the application of a TOWS matrix, by combining the internal and external factors, a list of possible strategies have been formulated to be used as a guide to decision-making related to the transition to a cloud environment.

Exploiting Structured Linked Data in Enterprise Knowledge Management Systems: An Idea Management Case Study

In parallel to the effort of creating Open Linked Data for the World Wide Web there is a number of projects aimed for developing the same technologies but in the context of their usage in closed environments such as private enterprises. In the paper, we present results of research on interlinking structured data for use in Idea Management Systems - a still rare breed of knowledge management systems dedicated to innovation management. In our study, we show the process of extending an ontology that initially covers only the Idea Management System structure towards the concept of linking with distributed enterprise data and public data using Semantic Web technologies. Furthermore we point out how the established links can help to solve the key problems of contemporary Idea Management Systems

Gi2MO: Interoperability, Linking and Filtering in Idea Management Systems

This thesis proposes how to apply the Semantic Web tech- nologies for the Idea Management Systems to deliver a solution to knowl- edge management and information over ow problems. Firstly, the aim is to present a model that introduces rich metadata annotations and their usage in the domain of Idea Management Systems. Furthermore, the the- sis shall investigate how to link innovation data with information from other systems and use it to categorize and lter out the most valuable elements. In addition, the thesis presents a Generic Idea and Innovation Management Ontology (Gi2MO) and aims to back its creation with a set of case studies followed by evaluations that prove how Semantic Web can work as tool to create new opportunities and leverage the contemporary Idea Management legacy systems into the next level.

Profiling for run-time checking of computational properties and performance debugging in logic programs

Although several profiling techniques for identifying performance bottlenecks in logic programs have been developed, they are generally not automatic and in most cases they do not provide enough information for identifying the root causes of such bottlenecks. This complicates using their results for guiding performance improvement. We present a profiling method and tool that provides such explanations. Our profiler associates cost centers to certain program elements and can measure different types of resource-related properties that affect performance, preserving the precedence of cost centers in the cali graph. It includes an automatic method for detecting procedures that are performance bottlenecks. The profiling tool has been integrated in a previously developed run-time checking framework to allow verification of certain properties when they cannot be verified statically. The approach allows checking global computational properties which require complex instrumentation tracking information about previous execution states, such as, e.g., that the execution time accumulated by a given procedure is not greater than a given bound. We have built a prototype implementation, integrated it in the Ciao/CiaoPP system and successfully applied it to performance improvement, automatic optimization (e.g., resource-aware specialization of programs), run-time checking, and debugging of global computational properties (e.g., resource usage) in Prolog programs.

Towards resource usage analysis of miniZinc models

We present a method for the static resource usage analysis of MiniZinc models. The analysis can infer upper bounds on the usage that a MiniZinc model will make of some resources such as the number of constraints of a given type (equality, disequality, global constraints, etc.), the number of variables (search variables or temporary variables), or the size of the expressions before calling the solver. These bounds are obtained from the models independently of the concrete input data (the instance data) and are in general functions of sizes of such data. In our approach, MiniZinc models are translated into Ciao programs which are then analysed by the CiaoPP system. CiaoPP includes a parametric analysis framework for resource usage in which the user can define resources and express the resource usage of library procedures (and certain program construets) by means of a language of assertions. We present the approach and report on a preliminary implementation, which shows the feasibility of the approach, and provides encouraging results.

An approach to profiling for run-time checking of computational properties and performance debugging in logic programs.

Although several profiling techniques for identifying performance bottlenecks in logic programs have been developed, they are generally not automatic and in most cases they do not provide enough information for identifying the root causes of such bottlenecks. This complicates using their results for guiding performance improvement. We present a profiling method and tool that provides such explanations. Our profiler associates cost centers to certain program elements and can measure different types of resource-related properties that affect performance, preserving the precedence of cost centers in the call graph. It includes an automatic method for detecting procedures that are performance bottlenecks. The profiling tool has been integrated in a previously developed run-time checking framework to allow verification of certain properties when they cannot be verified statically. The approach allows checking global computational properties which require complex instrumentation tracking information about previous execution states, such as, e.g., that the execution time accumulated by a given procedure is not greater than a given bound. We have built a prototype implementation, integrated it in the Ciao/CiaoPP system and successfully applied it to performance improvement, automatic optimization (e.g., resource-aware specialization of programs), run-time checking, and debugging of global computational properties (e.g., resource usage) in Prolog programs.

Integration of wearable devices in a wireless sensor network for an e-health application

Applications based on Wireless Sensor Networks for Internet of Things scenarios are on the rise. The multiple possibilities they offer have spread towards previously hard to imagine fields, like e-health or human physiological monitoring. An application has been developed for its usage in scenarios where data collection is applied to smart spaces, aiming at its usage in fire fighting and sports. This application has been tested in a gymnasium with real, non-simulated nodes and devices. A Graphic User Interface has been implemented to suggest a series of exercises to improve a sportsman/woman s condition, depending on the context and their profile. This system can be adapted to a wide variety of e-health applications with minimum changes, and the user will interact using different devices, like smart phones, smart watches and/or tablets.

Identification of intermediate debonding damage in FRP-plated RC beams based on multi-objective particle swarm optimization without updated baseline model

Fiber reinforced polymer composites (FRP) have found widespread usage in the repair and strengthening of concrete structures. FRP composites exhibit high strength-to-weight ratio, corrosion resistance, and are convenient to use in repair applications. Externally bonded FRP flexural strengthening of concrete beams is the most extended application of this technique. A common cause of failure in such members is associated with intermediate crack-induced debonding (IC debonding) of the FRP substrate from the concrete in an abrupt manner. Continuous monitoring of the concrete?FRP interface is essential to pre- vent IC debonding. Objective condition assessment and performance evaluation are challenging activities since they require some type of monitoring to track the response over a period of time. In this paper, a multi-objective model updating method integrated in the context of structural health monitoring is demonstrated as promising technology for the safety and reliability of this kind of strengthening technique. The proposed method, solved by a multi-objective extension of the particle swarm optimization method, is based on strain measurements under controlled loading. The use of permanently installed fiber Bragg grating (FBG) sensors embedded into the FRP-concrete interface or bonded onto the FRP strip together with the proposed methodology results in an automated method able to operate in an unsupervised mode.