Abstract:
Fourier domain optical coherence tomography (FD-OCT) requires resampling of spectrally resolved depth information from wavelength to wave number, and the subsequent application of the inverse Fourier transform. The display rates of OCT images are much slower than the image acquisition rates due to processing speed limitations on most computers. We demonstrate a real-time display of processed OCT images using a linear-in-wavenumber(linear-k) spectrometer and a graphics processing unit (GPU). We use the linear-k spectrometer with the combination of a diffractive grating with 1200 lines/mm and a F2 equilateral prism in the 840-nm spectral region to avoid calculating the resampling process. The calculations of the fast Fourier transform (FFT) are accelerated by the GPU with many stream processors, which realizes highly parallel processing. A display rate of 27.9 frames/sec for processed images (2048 FFT size x1000 lateral A-scans) is achieved in our OCT system using a line scan CCD camera operated at 27.9 kHz.

Abstract: 

Over the past several years, graphics processing units (GPUs) have gained interest as general purpose highly parallel coprocessors. Early adopters were forced to use traditional 3D graphics application programming interfaces (APIs) in order to access the computational power of the GPU. This process of recasting general purpose problems into graphical terms can be time consuming and create obscure code. The introduction of NVidia’s Compute Unified Device Architecture (CUDA) Framework, a C-language development environment for NVidia GPUs, is designed to ease the burden placed on the general purpose GPU programmer. In parallel with the CUDA release, NVidia also released implementations of the BLAS and FFT libraries for the GPU under the names CUBLAS and CUFFT, respectively.
   Previous research [1,2] has shown the vast computational power of GPUs for signal processing. Modern radar signal processing is a data parallel operation that benefits from parallel processing architectures. This investigation will focus on the real-world benefit of GPUs for radar pulse compression. First, the performance of 1D and 2D FFTs on a GPU via CUFFT will be compared to a modern day multi-core CPU implementation using FFTW [3]. Subsequently, these performance results will inform the implementation of two surrogate radar pulse compression chains, having differing processing complexity, which will also in turn be benchmarked similar to the FFTs. 

Abstract:  

The high-performance parallel processors in video accelerators, GPUs, can be used as numerical co-processors in a variety of applications. The RapidMind Development Platform is a software development system that allows the developer to use standard C++ programming to easily create high-performance and massively parallel applications that run on the GPU. Using the RapidMind platform, we compare the performance of FFT, BLAS dense matrix multiplication, and quasi-Monte Carlo option pricing benchmarks on the GPU against highly tuned CPU implementations. The advantages and limitations of GPU acceleration are discussed as well as techniques for optimizing performance.

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Development of software-and-hardware platform for creating digital models of "smart" industrial complexes and manufacturing control system.

Interested parties contact: neurocomputer@yandex.ru

Trends in the development of mining and processing industry indicate, that in the near future mainly "hard"; and remote territories will be developed, and also mineral ore deposits, which have a number of problematic physiographic, climatic and natural conditions, and other important features including remoteness of the territory and adverse natural conditions, complex geological and geophysical conditions, shortage on energy resources, lack of human resources and qualified staff, complex and insufficiently developed transport infrastructure.

To archieve the economic efficincy of the development of such facilities there is a serious need for deep-automated, "deserted" industries with elements of "artificial intelligence" - "smart" industrial complexes, (SIC) based on flexible quasi-module architecture. This requires the creation of computer models of complicated industrial complexes, intelligent control systems of technological, power and transportation manufacturing processes using embedded systems, SCADA, MES technologies and their intergration in the single technological platform applying in CAD/CAE and PLM systems.

Abstract:

This paper presents parallel FFT algorithms with different degree of computation and communication overheads for multiprocessors in a Network-on-Chip (NoC) environment. Of the three parallel FFT algorithms presented in this paper, we propose two parallel FFT algorithms for a 2D NoC that can contain a variable number of processing ele- ments (PEs) and one is a reference parallel FFT algorithm for comparison. A parallel FFT algorithm we propose increases performance by assigning well-balanced computa- tion tasks to PEs. The execution times are reduced because the algorithm uses data locality well to avoid unnecessary data exchanges among PEs and removes the overall idle periods by a balanced task scheduling. An enhanced version of this algorithm is suggested in which communication traffic is reduced. In this algorithm, returning trans- formed data to an original PE after one computation stage before sending them to a next PE for the following stage is removed. Instead, we propose a method that enables to keep regularity of the data communication and computations with twiddle factors. According to the simulation result from our cycle-accurate SystemC NoC model with a parametrizable 2-D mesh architecture, and the analysis of the algorithms in time and complexity, our proposed algorithms are shown to outperform the reference parallel FFT algorithm and FFT implementations on TI Digital Signal Processors (DSPs) that have similar specifications to our simulation environment.