Keynote: A Smart World: A Development Model for Intelligent Cities
Dr Azamat Abdoullaev, EIS Encyclopedic Intelligent Systems, Cyprus, EU; Moscow, RUSSIA
The 21st century smart sustainable development suggests the wholly new principles, strategies, and elements of sustainable living: a new set of eco-intelligent world strategies, models, policies, and solutions. It’s when the sustainable world’s intelligent urbanism is synergistically driven by natural capital, social capital and digital capital, like as the Internet/Web of Things, Knowledge and Social Intelligence and Renewable Energy Sources.
A genuine sustainable community is consistently defined as digitally smart, socially intelligent, and ecologically sustainable.
At the global level, the Smart World is modeled as a Smart Eco Planet of intelligent sustainable communities: countries, regions, cities, towns, villages, districts, and neighborhoods. The Smart Eco Planet is then all as about intelligent communities, smart natural ecosystems, digital smart economy, intelligent people, digital smart governance, smart transport and intellectual ICTs, eco-environments, eco-smart living and creative working in intelligent eco-buildings, cities, regions, countries, and global knowledge ecosystems.
A true Smart Sustainable City is accordingly redefined as an urban entity or city pattern with three critical parts/layers/levels/spaces, all planned, developed and managed as its integrated elements:
- Digital/ICT/Hi-Tech/Ubiquitous/Cyber City (Digital/Information Capital; Multi-Play Telecom Network, ICT spaces/systems/applications, Ubiquitous Computation, Network-integrated Real Estate, Virtual Lifestyle);
- Sustainable/Ecological/Green/Zero-Carbon/Zero-Waste/Eco Friendly/Solar City (Natural Capital; Green Energy Network, Real Eco Estate, Green Lifestyle);
- Knowledge/Learning/Innovation/ /Intelligent/Science/Intellectual/LivingLab/Creative/Human City/Noopolis (Knowledge Capital; Knowledge Triangle/Square/Grid/Ecology, Intelligent/Smart Lifestyle).
Modeled as the fully sustainable city, the Smart/Intelligent Eco City’s concept, design, planning and implementation is moving further on the Europe 2020 strategic priorities of smart sustainable and inclusive growth.
Keynote: Data Dependence Analysis Techniques for Multi-core Architectures
Professor Kleanthis Psarris, The University of Texas at San Antonio
In multi-core architectures large scale scientific applications have to be redesigned to efficiently use the multiple cores and deliver higher performance. Optimizing compilers rely upon program analysis techniques to detect data dependences between program statements, perform optimizations, and identify code fragments that can be executed concurrently. However, most data dependence tests are only able to analyze linear expressions, even though non-linear expressions occur frequently in practice. Therefore, considerable amounts of potential parallelism remained unexploited. In order to handle such complex instances of the dependence problem and increase program parallelization we developed new program analysis techniques. Our methods are based on a set of polynomial time techniques that can prove or disprove dependences in source codes with non-linear and symbolic expressions, complex loop bounds, arrays with coupled subscripts, and if-statement constraints. We performed an experimental evaluation of several data dependence tests and we compared them in terms of data dependence accuracy, compilation efficiency, effectiveness in parallelization and program execution performance. We run various experiments using the Perfect Club Benchmarks, the SPEC benchmarks, and the scientific library Lapack. We measured the accuracy and efficiently of each data dependence test. We also determined the impact of each data dependence test on the total compilation time. Finally, we measured the number of loops parallelized by each test and we compared the execution performance of each benchmark on a multi-core architecture. The experimental results indicate that our dependence analysis tool is accurate, efficient and more effective in program parallelization than past data dependence analysis techniques. The improved parallelization resulted into higher speedups and better program execution performance in several benchmarks.