ACE 16k based stand-alone system for real-time pre-processing tasks

This paper describes the design of a programmable stand-alone system for real time vision pre-processing tasks. The system's architecture has been implemented and tested using an ACE16k chip and a Xilinx xc4028xl FPGA. The ACE16k chip consists basically of an array of 128×128 identical mixed-signal processing units, locally interacting, which operate in accordance with single instruction multiple data (SIMD) computing architectures and has been designed for high speed image pre-processing tasks requiring moderate accuracy levels (7 bits). The input images are acquired using the optical input capabilities of the ACE16k chip, and after being processed according to a programmed algorithm, the images are represented at real time on a TFT screen. The system is designed to store and run different algorithms and to allow changes and improvements. Its main board includes a digital core, implemented on a Xilinx 4028 Series FPGA, which comprises a custom programmable Control Unit, a digital monochrome PAL video generator and an image memory selector. Video SRAM chips are included to store and access images processed by the ACE16k. Two daughter boards hold the program SRAM and a video DAC-mixer card is used to generate composite analog video signal.European Commission IST2001 – 38097Ministerio de Ciencia y Tecnología TIC2003 – 09817- C02 – 01Office of Naval Research (USA) N00014021088

Optical tomography using the SCIRun problem solving environment: Preliminary results for three-dimensional geometries and parallel processing

We present a 3D implementation of the UCL imaging package for absorption and scatter reconstruction from time-resolved data (TOAST), embedded in the SCIRun interactive simulation and visualization package developed at the University of Utah. SCIRun is a scientific programming environment that allows the interactive construction, debugging, and steering of large-scale scientific computations. While the capabilities of SCIRun's interactive approach are not yet fully exploited in the current TOAST implementation, an immediate benefit of the combined TOAST/SCIRun package is the availability of optimized parallel finite element forward solvers, and the use of SCIRun's existing 3D visualisation tools. A reconstruction of a segmented 3D head model is used as an example for demonstrating the capability of TOAST/SCIRun of simulating anatomically shaped meshes

Peachy Parallel Assignments (EduHPC 2018)

Peachy Parallel Assignments are a resource for instructors teaching parallel and distributed programming. These are high-quality assignments, previously tested in class, that are readily adoptable. This collection of assignments includes implementing a subset of OpenMP using pthreads, creating an animated fractal, image processing using histogram equalization, simulating a storm of high-energy particles, and solving the wave equation in a variety of settings. All of these come with sample assignment sheets and the necessary starter code.Departamento de Informática (Arquitectura y Tecnología de Computadores, Ciencias de la Computación e Inteligencia Artificial, Lenguajes y Sistemas Informáticos)Facilitar la inclusión de ejercicios prácticos de programación paralela en cursos de Computación Paralela o de alto rendimiento (HPC)Comunicación en congreso: Descripción de ejercicios prácticos con acceso a material ya desarrollado y probado

Asynchronous Execution of Python Code on Task Based Runtime Systems

Despite advancements in the areas of parallel and distributed computing, the complexity of programming on High Performance Computing (HPC) resources has deterred many domain experts, especially in the areas of machine learning and artificial intelligence (AI), from utilizing performance benefits of such systems. Researchers and scientists favor high-productivity languages to avoid the inconvenience of programming in low-level languages and costs of acquiring the necessary skills required for programming at this level. In recent years, Python, with the support of linear algebra libraries like NumPy, has gained popularity despite facing limitations which prevent this code from distributed runs. Here we present a solution which maintains both high level programming abstractions as well as parallel and distributed efficiency. Phylanx, is an asynchronous array processing toolkit which transforms Python and NumPy operations into code which can be executed in parallel on HPC resources by mapping Python and NumPy functions and variables into a dependency tree executed by HPX, a general purpose, parallel, task-based runtime system written in C++. Phylanx additionally provides introspection and visualization capabilities for debugging and performance analysis. We have tested the foundations of our approach by comparing our implementation of widely used machine learning algorithms to accepted NumPy standards