Large Scale Document Inversion using a Multi-threaded Computing System.

Current microprocessor architecture is moving towards multi-core/multi-threaded systems. This trend has led to a surge of interest in using multi-threaded computing devices, such as the Graphics Processing Unit (GPU), for general purpose computing. We can utilize the GPU in computation as a massive parallel coprocessor because the GPU consists of multiple cores. The GPU is also an affordable, attractive, and user-programmable commodity. Nowadays a lot of information has been flooded into the digital domain around the world. Huge volume of data, such as digital libraries, social networking services, e-commerce product data, and reviews, etc., is produced or collected every moment with dramatic growth in size. Although the inverted index is a useful data structure that can be used for full text searches or document retrieval, a large number of documents will require a tremendous amount of time to create the index. The performance of document inversion can be improved by multi-thread or multi-core GPU. Our approach is to implement a linear-time, hash-based, single program multiple data (SPMD), document inversion algorithm on the NVIDIA GPU/CUDA programming platform utilizing the huge computational power of the GPU, to develop high performance solutions for document indexing. Our proposed parallel document inversion system shows 2-3 times faster performance than a sequential system on two different test datasets from PubMed abstract and e-commerce product reviews. CCS CONCEPTS: •Information systems➝Information retrieval • Computing methodologies➝Massively parallel and high-performance simulations.

3D Modeling of Branching Structures for Anatomical Instruction.

Branching tubular structures are prevalent in many different organic and synthetic settings. From trees and vegetation in nature, to vascular structures throughout human and animal biology, these structures are always candidates for new methods of graphical and visual expression. We present a modeling tool for the creation and interactive modification of these structures. Parameters such as thickness and position of branching structures can be modified, while geometric constraints ensure that the resulting mesh will have an accurate anatomical structure by not having inconsistent geometry. We apply this method to the creation of accurate representations of the different types of retinal cells in the human eye. This method allows a user to quickly produce anatomically accurate structures with low polygon counts that are suitable for rendering at interactive rates on commodity computers and mobile devices.

Biological Sequence Mining Using Plausible Neural Network and its Application to Exon/intron Boundaries Prediction.

Biological sequence usually contains yet to find knowledge, and mining biological sequences usually involves a huge dataset and long computation time. Common tasks for biological sequence mining are pattern discovery, classification and clustering. The newly developed model, Plausible Neural Network (PNN), provides an intuitive and unified architecture for such a large dataset analysis. This paper introduces the basic concepts of the PNN, and explains how it is applied to biological sequence mining. The specific task of biological sequence mining, exon/intron prediction, is implemented by using PNN. The experimental results show the capability of solving biological sequence mining tasks using PNN.