Massively parallel processor

A brief description is given of the Massively Parallel Processor (MPP). Major applications of the MPP are in the area of image processing (where the operands are often very small integers) from very high spatial resolution passive image sensors, signal processing of radar data, and numerical modeling simulations of climate. The system can be programmed in assembly language or a high level language. Information on background, status, architecture, programming, hardware reliability, applications, and the MPP's development as a national resource for parallel algorithm research are presented in outline form

A New Tool for Intelligent Parallel Processing of Radar/SAR Remotely Sensed Imagery

A novel parallel tool for large-scale image enhancement/reconstruction and postprocessing of radar/SAR sensor systems is addressed. The proposed parallel tool performs the following intelligent processing steps: image formation, for the application of different system-level effects of image degradation with a particular remote sensing (RS) system and simulation of random noising effects, enhancement/reconstruction by employing nonparametric robust high-resolution techniques, and image postprocessing using the fuzzy anisotropic diffusion technique which incorporates a better edge-preserving noise removal effect and faster diffusion process. This innovative tool allows the processing of high-resolution images provided with different radar/SAR sensor systems as required by RS endusers for environmental monitoring, risk prevention, and resource management. To verify the performance implementation of the proposed parallel framework, the processing steps are developed and specifically tested on graphic processing units (GPU), achieving considerable speedups compared to the serial version of the same techniques implemented in C language

Edgar : a platform for hardware/software codesign

Codesign is a unified methodology to develop complex systems with hardware and software components. EDgAR, a platform for hardware/software codesign is described, which is intended to prototype complex digital systems. It employs programmable logic devices (MACHs and FPGAs) and a transputer-based parallel architecture. This platform and its associated methodology reduce the systems production cost, decreasing the time for the design and the test of the prototypes. The EDgAR supporting tools are introduced, which were conceived to specify systems at an high-level of abstraction, with a standard language and to allow a high degree of automation on the synthesis process. This platform was used to emulate an integrated circuit for image processing purposes

Achieving High-Performance the Functional Way: A Functional Pearl on Expressing High-Performance Optimizations as Rewrite Strategies

Optimizing programs to run efficiently on modern parallel hardware is hard but crucial for many applications. The predominantly used imperative languages - like C or OpenCL - force the programmer to intertwine the code describing functionality and optimizations. This results in a portability nightmare that is particularly problematic given the accelerating trend towards specialized hardware devices to further increase efficiency. Many emerging DSLs used in performance demanding domains such as deep learning or high-performance image processing attempt to simplify or even fully automate the optimization process. Using a high-level - often functional - language, programmers focus on describing functionality in a declarative way. In some systems such as Halide or TVM, a separate schedule specifies how the program should be optimized. Unfortunately, these schedules are not written in well-defined programming languages. Instead, they are implemented as a set of ad-hoc predefined APIs that the compiler writers have exposed. In this functional pearl, we show how to employ functional programming techniques to solve this challenge with elegance. We present two functional languages that work together - each addressing a separate concern. RISE is a functional language for expressing computations using well known functional data-parallel patterns. ELEVATE is a functional language for describing optimization strategies. A high-level RISE program is transformed into a low-level form using optimization strategies written in ELEVATE . From the rewritten low-level program high-performance parallel code is automatically generated. In contrast to existing high-performance domain-specific systems with scheduling APIs, in our approach programmers are not restricted to a set of built-in operations and optimizations but freely define their own computational patterns in RISE and optimization strategies in ELEVATE in a composable and reusable way. We show how our holistic functional approach achieves competitive performance with the state-of-the-art imperative systems Halide and TVM

A Multi-Threaded Fast Convolver for Dynamically Parallel Image Filtering

2D convolution is a staple of digital image processing. The advent of large format imagers makes it possible to literally ``pave'' with silicon the focal plane of an optical sensor, which results in very large images that can require a significant amount computation to process. Filtering of large images via 2D convolutions is often complicated by a variety of effects (e.g., non-uniformities found in wide field of view instruments). This paper describes a fast (FFT based) method for convolving images, which is also well suited to very large images. A parallel version of the method is implemented using a multi-threaded approach, which allows more efficient load balancing and a simpler software architecture. The method has been implemented within in a high level interpreted language (IDL), while also exploiting open standards vector libraries (VSIPL) and open standards parallel directives (OpenMP). The parallel approach and software architecture are generally applicable to a variety of algorithms and has the advantage of enabling users to obtain the convenience of an easy operating environment while also delivering high performance using a fully portable code.Comment: 25 pages including color figures. Submitted to the Journal of Parallel and Distributed Computin