Spatial Data Science: Closing the human-spatial computing-environment loop

Over the last decade, the term spatial computing has grown to have two different, though not entirely unrelated, definitions. The first definition of spatial computing stems from industry, where it refers primarily to new kinds of augmented, virtual, mixed-reality, and natural user interface technologies. A second definition coming out of academia takes a broader perspective that includes active research in geographic information science as well as the aforementioned novel UI technologies. Both senses reflect an ongoing shift toward increased interaction with computing interfaces and sensors embedded in the environment and how the use of these technologies influence how we behave and make sense of and even change the world we live in. Regardless of the definition, research in spatial computing is humming along nicely without the need to identify new research agendas or new labels for communities of researchers. However, as a field of research, it could be helpful to view spatial data science as the glue that coheres spatial computing with problem-solving and learning in the real world into a more holistic discipline.Comment: 2 pages, Spatial Data Science Symposiu

Accelerating legacy applications with spatial computing devices

Heterogeneous computing is the major driving factor in designing new energy-efficient high-performance computing systems. Despite the broad adoption of GPUs and other specialized architectures, the interest in spatial architectures like field-programmable gate arrays (FPGAs) has grown. While combining high performance, low power consumption and high adaptability constitute an advantage, these devices still suffer from a weak software ecosystem, which forces application developers to use tools requiring deep knowledge of the underlying system, often leaving legacy code (e.g., Fortran applications) unsupported. By realizing this, we describe a methodology for porting Fortran (legacy) code on modern FPGA architectures, with the target of preserving performance/power ratios. Aimed as an experience report, we considered an industrial computational fluid dynamics application to demonstrate that our methodology produces synthesizable OpenCL codes targeting Intel Arria10 and Stratix10 devices. Although performance gain is not far beyond that of the original CPU code (we obtained a relative speedup of x 0.59 and x 0.63, respectively, for a single optimized main kernel, while only on the Stratix10 we achieved x 2.56 by replicating the main optimized kernel 4 times), our results are quite encouraging to drawn the path for further investigations. This paper also reports some major criticalities in porting Fortran code on FPGA architectures

Applying Spatial Computing to Everyday Interactive Designs

In this position paper, we address the applicability of spatial computing in the field of interactive architecture. The process of designing large-scale interactive systems is cumbersome, due in fact to single design cycles (transforming ideas into prototypes) taking a period of time usually measured in months, most of it dedicated to writing the software controlling the system. As most interactive architecture projects pass through several design cycles interleaved with user studies, speeding up the generation of the needed software becomes of crucial importance. The global-to-local programming approach is in fact a perfect tool for this task. Describing complex behaviors with simple rules is rarely seen in the existing installations, the large majority employing a central computer for the control of the system. Building up on our previous experience in this area, we created HiveKit, a proof of concept allowing bridging between the two fields, giving non-specialists easy access to distributed algorithms. HiveKit is a software package which allows designers to specify the desired behavior and automatically generate and deploy the needed code on networks of embedded devices. We introduce several projects where HiveKit is employed and create an argument, based on user studies, favoring the need for large-scale adoption of such tools

Low-power spatial computing using dynamic threshold devices

Asynchronous spatial computing systems exhibit only localized communication, their overall data-flow being controlled by handshaking. It is therefore straightforward to determine when a particular part of such a system is active. We show that using thin-body double-gate fully depleted SOI transistors, the shift in threshold voltage that can be produced by modulating the back-gate bias is sufficient to reduce subthreshold leakage power by a factor of more than 104 in typical circuits. Using TBFDSOI devices in spatial computing architectures will allow overall power to be greatly reduced while maintaining high performance

Web service based Grid workflow application in quantitative remote sensing retrieval

Along with the unprecedented data-collecting capability, the higher algorithm accuracy and real-time application requirements, redundant spatial computing model had been implemented. Traditionally these spatial computing models are stored in different application centers. To avoid waste of resource, Grid workflow provides a powerful tool for sharing both remote sensing data and processing middleware. In order to enhance the interoperability of the heterogeneous quantitative remote sensing retrieval model in the Grid workflow environment, we propose a web service based Grid workflow framework to improve this situation. According to the Open Geospatial Consortium (OGC) and web service standards, we implement a prototype of this framework. Through the experiment, we can find that web service can work well with Grid workflow and provide a management ability of remote sensing model. Also this approach can separate the application logic and process logic, providing the interoperability ability both in application and process layers

Acceleration of Computational Geometry Algorithms for High Performance Computing Based Geo-Spatial Big Data Analysis

Geo-Spatial computing and data analysis is the branch of computer science that deals with real world location-based data. Computational geometry algorithms are algorithms that process geometry/shapes and is one of the pillars of geo-spatial computing. Real world map and location-based data can be huge in size and the data structures used to process them extremely big leading to huge computational costs. Furthermore, Geo-Spatial datasets are growing on all V’s (Volume, Variety, Value, etc.) and are becoming larger and more complex to process in-turn demanding more computational resources. High Performance Computing is a way to breakdown the problem in ways that it can run in parallel on big computers with massive processing power and hence reduce the computing time delivering the same results but much faster.This dissertation explores different techniques to accelerate the processing of computational geometry algorithms and geo-spatial computing like using Many-core Graphics Processing Units (GPU), Multi-core Central Processing Units (CPU), Multi-node setup with Message Passing Interface (MPI), Cache optimizations, Memory and Communication optimizations, load balancing, Algorithmic Modifications, Directive based parallelization with OpenMP or OpenACC and Vectorization with compiler intrinsic (AVX). This dissertation has applied at least one of the mentioned techniques to the following problems. Novel method to parallelize plane sweep based geometric intersection for GPU with directives is presented. Parallelization of plane sweep based Voronoi construction, parallelization of Segment tree construction, Segment tree queries and Segment tree-based operations has been presented. Spatial autocorrelation, computation of getis-ord hotspots are also presented. Acceleration performance and speedup results are presented in each corresponding chapter

Spatial computing per smart devices

Magic Carpet, nato come un middleware orientato a una dimostrazione sullo spatial computing, che inizialmente coinvolgeva solo smart devices ed un tappeto di tag NFC, è il punto di partenza per uno studio sulle tecnologie abilitanti in tale campo. Il prodotto finale è una toolchain per lo sviluppo e la distribuzione, su dispositivi connessi, di applicazioni di spatial computing. Essa comprende un interprete per un DSL basato su un core calculus formalizzato, Field Calculus, e un middleware che supporta l'astrazione curando, a basso livello, le comunicazioni con il vicinato e le percezioni ambientali