Solution of partial differential equations on vector and parallel computers

The present status of numerical methods for partial differential equations on vector and parallel computers was reviewed. The relevant aspects of these computers are discussed and a brief review of their development is included, with particular attention paid to those characteristics that influence algorithm selection. Both direct and iterative methods are given for elliptic equations as well as explicit and implicit methods for initial boundary value problems. The intent is to point out attractive methods as well as areas where this class of computer architecture cannot be fully utilized because of either hardware restrictions or the lack of adequate algorithms. Application areas utilizing these computers are briefly discussed

Design of testbed and emulation tools

The research summarized was concerned with the design of testbed and emulation tools suitable to assist in projecting, with reasonable accuracy, the expected performance of highly concurrent computing systems on large, complete applications. Such testbed and emulation tools are intended for the eventual use of those exploring new concurrent system architectures and organizations, either as users or as designers of such systems. While a range of alternatives was considered, a software based set of hierarchical tools was chosen to provide maximum flexibility, to ease in moving to new computers as technology improves and to take advantage of the inherent reliability and availability of commercially available computing systems

Parallel Computers in Signal Processing

Signal processing often requires a great deal of raw computing power for which it is important to take a look at parallel computers. The paper reviews various types of parallel computer architectures from the viewpoint of signal and image processing

Research summary, January 1989 - June 1990

The Research Institute for Advanced Computer Science (RIACS) was established at NASA ARC in June of 1983. RIACS is privately operated by the Universities Space Research Association (USRA), a consortium of 62 universities with graduate programs in the aerospace sciences, under a Cooperative Agreement with NASA. RIACS serves as the representative of the USRA universities at ARC. This document reports our activities and accomplishments for the period 1 Jan. 1989 - 30 Jun. 1990. The following topics are covered: learning systems, networked systems, and parallel systems

Satellite on-board processing for earth resources data

Results of a survey of earth resources user applications and their data requirements, earth resources multispectral scanner sensor technology, and preprocessing algorithms for correcting the sensor outputs and for data bulk reduction are presented along with a candidate data format. Computational requirements required to implement the data analysis algorithms are included along with a review of computer architectures and organizations. Computer architectures capable of handling the algorithm computational requirements are suggested and the environmental effects of an on-board processor discussed. By relating performance parameters to the system requirements of each of the user requirements the feasibility of on-board processing is determined for each user. A tradeoff analysis is performed to determine the sensitivity of results to each of the system parameters. Significant results and conclusions are discussed, and recommendations are presented

Advanced flight control system study

The architecture, requirements, and system elements of an ultrareliable, advanced flight control system are described. The basic criteria are functional reliability of 10 to the minus 10 power/hour of flight and only 6 month scheduled maintenance. A distributed system architecture is described, including a multiplexed communication system, reliable bus controller, the use of skewed sensor arrays, and actuator interfaces. Test bed and flight evaluation program are proposed

Multiprocessing techniques for unmanned multifunctional satellites Final report,

Simulation of on-board multiprocessor for long lived unmanned space satellite contro

Survey of Parallel Processing on Big Data

No doubt we are entering the big data epoch. The datasets have gone from small to super large scale, which not only brings us benefits but also some challenges. It becomes more and more difficult to handle them with traditional data processing methods. Many companies have started to invest in parallel processing frameworks and systems for their own products because the serial methods cannot feasibly handle big data problems. The parallel database systems, MapReduce, Hadoop, Pig, Hive, Spark, and Twister are some examples of these products. Many of these frameworks and systems can handle different kinds of big data problems, but none of them can cover all the big data issues. How to wisely use existing parallel frameworks and systems to deal with large-scale data becomes the biggest challenge. We investigate and analyze the performance of parallel processing for big data. We review and analyze various parallel processing architectures and frameworks, and their capabilities for large-scale data. We also present the potential challenges on multiple techniques according to the characteristics of big data. At last, we present possible solutions for those challenges