Cost-effective HPC clustering for computer vision applications

We will present a cost-effective and flexible realization of high performance computing (HPC) clustering and its potential in solving computationally intensive problems in computer vision. The featured software foundation to support the parallel programming is the GNU parallel Knoppix package with message passing interface (MPI) based Octave, Python and C interface capabilities. The implementation is especially of interest in applications where the main objective is to reuse the existing hardware infrastructure and to maintain the overall budget cost. We will present the benchmark results and compare and contrast the performances of Octave and MATLAB

The Design of a System Architecture for Mobile Multimedia Computers

This chapter discusses the system architecture of a portable computer, called Mobile Digital Companion, which provides support for handling multimedia applications energy efficiently. Because battery life is limited and battery weight is an important factor for the size and the weight of the Mobile Digital Companion, energy management plays a crucial role in the architecture. As the Companion must remain usable in a variety of environments, it has to be flexible and adaptable to various operating conditions. The Mobile Digital Companion has an unconventional architecture that saves energy by using system decomposition at different levels of the architecture and exploits locality of reference with dedicated, optimised modules. The approach is based on dedicated functionality and the extensive use of energy reduction techniques at all levels of system design. The system has an architecture with a general-purpose processor accompanied by a set of heterogeneous autonomous programmable modules, each providing an energy efficient implementation of dedicated tasks. A reconfigurable internal communication network switch exploits locality of reference and eliminates wasteful data copies

Vision Science and Technology at NASA: Results of a Workshop

A broad review is given of vision science and technology within NASA. The subject is defined and its applications in both NASA and the nation at large are noted. A survey of current NASA efforts is given, noting strengths and weaknesses of the NASA program

Characterizing Deep-Learning I/O Workloads in TensorFlow

The performance of Deep-Learning (DL) computing frameworks rely on the performance of data ingestion and checkpointing. In fact, during the training, a considerable high number of relatively small files are first loaded and pre-processed on CPUs and then moved to accelerator for computation. In addition, checkpointing and restart operations are carried out to allow DL computing frameworks to restart quickly from a checkpoint. Because of this, I/O affects the performance of DL applications. In this work, we characterize the I/O performance and scaling of TensorFlow, an open-source programming framework developed by Google and specifically designed for solving DL problems. To measure TensorFlow I/O performance, we first design a micro-benchmark to measure TensorFlow reads, and then use a TensorFlow mini-application based on AlexNet to measure the performance cost of I/O and checkpointing in TensorFlow. To improve the checkpointing performance, we design and implement a burst buffer. We find that increasing the number of threads increases TensorFlow bandwidth by a maximum of 2.3x and 7.8x on our benchmark environments. The use of the tensorFlow prefetcher results in a complete overlap of computation on accelerator and input pipeline on CPU eliminating the effective cost of I/O on the overall performance. The use of a burst buffer to checkpoint to a fast small capacity storage and copy asynchronously the checkpoints to a slower large capacity storage resulted in a performance improvement of 2.6x with respect to checkpointing directly to slower storage on our benchmark environment.Comment: Accepted for publication at pdsw-DISCS 201

Efficient completeness inspection using real-time 3D color reconstruction with a dual-laser triangulation system

In this chapter, we present the final system resulting from the European Project \u201d3DComplete\u201d aimed at creating a low-cost and flexible quality inspection system capable of capturing 2.5D color data for completeness inspection. The system uses a single color camera to capture at the same time 3D data with laser triangulation and color texture with a special projector of a narrow line of white light, which are then combined into a color 2.5D model in real-time. Many examples of completeness inspection tasks are reported which are extremely difficult to analyze with state-of-the-art 2D-based methods. Our system has been integrated into a real production environment, showing that completeness inspection incorporating 3D technology can be readily achieved in a short time at low costs