With the proliferation of large, irregular, and sparse relational datasets, new storage and analysis platforms have arisen to fill gaps in performance and capability left by conventional approaches built on traditional database technologies and query languages. Many of these platforms apply graph structures and analysis techniques to enable users to ingest, update, query, and compute on the topological structure of the network represented as sets of edges relating sets of vertices. To store and process Facebook-scale datasets, software and algorithms must be able to support data sources with billions of edges, update rates of millions of updates per second, and complex analysis kernels. These platforms must provide intuitive interfaces that enable graph experts and novice programmers to write implementations of common graph algorithms. In this paper, we conduct a qualitative study and a performance comparison of 12 open source graph databases using four fundamental graph algorithms on networks containing up to 256 million edges.

An integrated optic interferometer for detecting foodborne pathogens was developed. The interferometer is a planar waveguide with two thin antibody-coated channels of immunochemically selective agents that interact with antigen molecules. One channel is coated with antibody to Salmonella as a sample, and the other is coated with human immunoglobulin G as a reference channel by using reductive amination. Salmonella was introduced onto the sensing channels through the flow cell on the channels. Phase shift (pi) generated by refractive index variation, as determined by interfering the perturbed sample channel with an unperturbed reference channel and observing the fringe shift, was used for detection. Salmonella Typhimurium (heat-treated or boiled) was detected by binding to antibody against Salmonella common structural antigen immobilized on a silane-derived sensor surface at concentrations in the range of 1x10(5) to 1x10(7) CFU/ml. Salmonella (1x10(7) CFU/ml) mixed with Escherichia coli (1x10(7) CFU/ml) were readily detected without any decrease in sensitivity by the direct assay. Application of a sandwich assay with a second antibody or a gold-conjugated antibody increased the detection limit to 1x10(5) CFU/ml within a 10-min reaction time. Various methods for the immobilization of the capture antibody to the biosensor channels were compared. The greatest binding response was observed in a direct reductive amination method with a long reaction period and increased the detection limit of direct binding of Salmonella antigen to 1x10(4) CFU/ml. The biosensor was able to detect Salmonella Typhimurium in chicken carcass wash fluid originally inoculated at a level of 20 CFU/ml after 12 h of nonselective enrichment. The planar optic biosensor shows promise as a fast, sensitive, reliable, and economical means of detecting food pathogens in the future.

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Graphics processing units (GPUs) are a powerful tool The programmability of the most recent generation of for numerical computation. The GPU architecture and GPUs creates the opportunity to develop very powerful, low computational model are uniquely designed for high-resolution cost accelerators for key radar signal processing algorithms. In high-speed grid-based calculations. This capability can be this paper, we describe an experiment in the application of utilized to accelerate certain classes of compute-intensive radar GPUs to the two-dimensional phase unwrapping problem at signal processing algorithms. Characteristics of a problem wellsuited for computation on a GPU include high levels of data the heart of interferometric synthetic aperture radar (IFSAR) parallelism, low control logic, uniform boundary conditions, and processing. While phase unwrapping is relatively simple in well-defined input and output. one dimension, it becomes quite complex in multiple dimensions. One approach to the problem of recovering the We describe the implementation of two-dimensional multigrid * -least-squares weighted phase unwrapping on a GPU and original phase from a measurement of wrapped phase in the demonstrate a large speedup over C and MATLAB presence of noise or other distortions casts it into the implementations. Details of the GPU computation are provided. mathematical framework of a solution to the discretized Background information on the GPU architecture and its Poisson's equation [2]. The resulting least squares solution for applicability to general-purpose computation is discussed. the weighted phase unwrapping problem involves the use of an iterative solution technique requiring only scalar add, subtract, multiply, and divide operations at each step, and is

QR decomposition is a computationally intensive linear algebra operation that factors a matrix A into the product of a unitary matrix Q and upper triangular matrix R. Adaptive systems commonly employ QR decomposition to solve overdetermined least squares problems. Performance of QR decomposition is typically the crucial factor limiting problem sizes.Graphics Processing Units (GPUs) are high-performance processors capable of executing hundreds of floating point operations in parallel. As commodity accelerators for 3D graphics, GPUs offer tremendous computational performance at relatively low costs. While GPUs are favorable to applications with much inherent parallelism requiring coarse-grain synchronization between processors, methods for efficiently utilizing GPUs for algorithms computing QR decomposition remain elusive.In this paper 1 , we discuss the architectural characteristics of GPUs and explain how a high-performance implementation of QR decomposition may be implemented. We provide detailed performance analysis of the resulting implementation for real-valued matrices and offer recommendations for achieving high performance to future developers of dense linear algebra procedures for GPUs. Our implementation sustains 143 GFLOP/s, and we believe this is the fastest announced QR implementation executing entirely on the GPU.