Evaluation of Design Tools for Rapid Prototyping of Parallel Signal Processing Algorithms

Digital signal processing (DSP) has become a popular method for handling not only signal processing, but communications, and control system applications. A DSP application of interest to the Air Force is high speed avionics processing. The real time computing requirements of avionics processing exceed the capabilities of current single chip DSP processors, and parallelization of multiple DSP processors is a solution to handle such requirements. Designing and implementing a parallel DSP algorithm has been a lengthy process often requiring different design tools and extensive programming experience. Through the use of integrated software development tools, rapid prototyping becomes possible by simulating algorithms, generating code for workstations or DSP microprocessors, and generating hardware description language code for hardware synthesis. This research examines the use of one such tool, the Signal Processing WorkSystem (SPW) by the Alta Group of Cadence Design Systems, Inc., and how SPW supports the rapid prototyping process from an avionics algorithm design through simulation and hardware implementation. Throughout this process, SPW is evaluated as an aid to the avionics designer to meet design objectives and evaluate tradeoffs to find the best blend of efficiency and effectiveness. By designing a two dimensional fast Fourier transform algorithm as a specific avionics algorithm and exploring implementation options, SPW is shown to be a viable rapid prototyping solution allowing an avionics designer to focus on design trade-offs instead of implementation details while using parallelization to meet real-time application requirements

Fast signal processing

Zvětšující se množství dat v moderním zpracování obrazu vyžaduje nový postupy v psaní algoritmů. Největší překážkou pro úspěšné zrychlení algoritmu je paralelizace a následná optimalizace. Programy jako CUDA a OpenCL s modifikovaným programovacím jazykem a rozhraním pomáhají s tímto problémem a otevírají paralelní zpracování širšímu okruhu lidí. V této práci zabývám základy zpracování obrazu a tomu jak paralelizace algoritmů může urychlit zpracování obrazu.An increasing amount of data in modern image processing requires a new approach in algorithms. The biggest obstacle for successful speed up of an algorithm is parallelization and subsequent optimization. Architectures like CUDA and OpenCL with modified programing languages and interfaces help to overcome this obstacle and bring parallel computing to a broader audience. In this paper I take a look at basics of image processing and how parallelization can speed up the algorithms in image processing.