Counting Triangles in Large Graphs on GPU

The clustering coefficient and the transitivity ratio are concepts often used in network analysis, which creates a need for fast practical algorithms for counting triangles in large graphs. Previous research in this area focused on sequential algorithms, MapReduce parallelization, and fast approximations. In this paper we propose a parallel triangle counting algorithm for CUDA GPU. We describe the implementation details necessary to achieve high performance and present the experimental evaluation of our approach. Our algorithm achieves 8 to 15 times speedup over the CPU implementation and is capable of finding 3.8 billion triangles in an 89 million edges graph in less than 10 seconds on the Nvidia Tesla C2050 GPU.Comment: 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW

The economic importance of cross-sectional technologies: An input-output approach

Technologies that are used in different production processes of miscellaneous industries are known as cross-sectional technologies. The economic importance of these technologies normally is measured by economic benchmarks of the producing industry. However, the impact of these tech-nologies for the whole economy is often not exactly known. By using a modified input-output ap-proach, it is shown for the welding technology how much the economic importance of a cross-sectional technology exceeds the economic importance of the technology producing industry.Cross-sectional technologies; benchmark; input-output analysis; decomposition; multi-pliers

Smart Sensing Systems for the Daily Drive

When driving, you might sometimes wonder, "Are there any disruptions on my regular route that might delay me, and will I be able to find a parking space when I arrive?" Two smartphone-based prototype systems can help answer these questions. The first is ParkSense, which can be used to sense on-street parking-space occupancy when coupled with electronic parking payment systems. The second system can sense and recognize a user's repeated car journeys, which can be used to provide personalized alerts to the user. Both systems aim to minimize the impact of sensing tasks on the device's lifetime so that the user can continue to use the device for its primary purpose. This department is part of a special issue on smart vehicle spaces