The Message Passing Interface MPI can be used as a portable, high-performance programming model for wide-area computing systems. The wide-area environment i n troduces challenging problems for the MPI implementor, due to the heterogeneity of both the underlying physical infrastructure and the software environment at di erent sites. In this article, we describe an MPI implementation that incorporates solutions to these problems. This implementation has been constructed by extending the Argonne MPICH implementation of MPI to use communication services provided by the Nexus communication library and authentication, resource allocation, process creation management, and information services provided by the I-Soft system initially and the Globus metacomputing toolkit work in progress. Nexus provides multimethod communication mechanisms that allow m ultiple communication methods to be used in a single computation with a uniform interface; I-Soft and Globus provided standard authentication, resource management, and process management mechanisms. We describe how these various mechanisms are supported in the Nexus implementation of MPI and present performance results for this implementation on multicomputers and networked systems. We also discuss how more advanced services provided by the Globus metacomputing toolkit are being used to construct a second-generation wide-area MPI.
Deep neural networks (DNNs) are successful in many computer vision tasks. However, the most accurate DNNs require millions of parameters and operations, making them energy, computation and memory intensive. This impedes the deployment of large DNNs in low-power devices with limited compute resources. Recent research improves DNN models by reducing the memory requirement, energy consumption, and number of operations without significantly decreasing the accuracy. This paper surveys the progress of low-power deep learning and computer vision, specifically in regards to inference, and discusses the methods for compacting and accelerating DNN models. The techniques can be divided into four major categories: (1) parameter quantization and pruning, (2) compressed convolutional filters and matrix factorization, (3) network architecture search, and (4) knowledge distillation. We analyze the accuracy, advantages, disadvantages, and potential solutions to the problems with the techniques in each category. We also discuss new evaluation metrics as a guideline for future research.
Computer vision has achieved impressive progress in recent years. Meanwhile, mobile phones have become the primary computing platforms for millions of people. In addition to mobile phones, many autonomous systems rely on visual data for making decisions and some of these systems have limited energy (such as unmanned aerial vehicles also called drones and mobile robots). These systems rely on batteries and energy efficiency is critical. This article serves two main purposes: (1) Examine the state-of-the-art for low-power solutions to detect objects in images. Since 2015, the IEEE Annual International Low-Power Image Recognition Challenge (LPIRC) has been held to identify the most energy-efficient computer vision solutions. This article summarizes 2018 winners' solutions. (2) Suggest directions for research as well as opportunities for low-power computer vision.
Abstract-One of the greatest technological improvements in recent years is the rapid progress using machine learning for processing visual data. Among all factors that contribute to this development, datasets with labels play crucial roles. Several datasets are widely reused for investigating and analyzing different solutions in machine learning. Many systems, such as autonomous vehicles, rely on components using machine learning for recognizing objects. This paper compares different visual datasets and frameworks for machine learning. The comparison is both qualitative and quantitative and investigates object detection labels with respect to size, location, and contextual information. This paper also presents a new approach creating datasets using real-time, geo-tagged visual data, greatly improving the contextual information of the data. The data could be automatically labeled by cross-referencing information from other sources (such as weather).
Remote method invocation (RMI) is available in the current Java language design and implementation, providing the much‐needed capability of allowing objects running in different Java processes to collaborate using a variation on the popular remote procedure call (RPC). Although RMI provides features which are desirable for high‐performance distributed computing, its design and implementation are deficient in key areas of importance to the high‐performance computing community in general. This paper addresses the key deficiencies of RMI and how these deficiencies affect the design and implementation of distributed object applications. Reflective RMI (RRMI) is an open RMI implementation which makes better use of the object‐oriented features of Java. RRMI is so‐called reflective because it directly employs the reflection capabilities of the current Java language to invoke methods remotely. RRMI makes use of the dynamic class loader (a class called NetClassLoader) to allow client/server applications to be built for high‐performance computing systems without having all of the .class files present on all nodes in a parallel computation. Among other features discussed are support for asynchronous remote method invocations with deferred reply and exception semantics. © 1998 John Wiley & Sons, Ltd.
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