NASA high performance computing and communications program

The National Aeronautics and Space Administration's HPCC program is part of a new Presidential initiative aimed at producing a 1000-fold increase in supercomputing speed and a 100-fold improvement in available communications capability by 1997. As more advanced technologies are developed under the HPCC program, they will be used to solve NASA's 'Grand Challenge' problems, which include improving the design and simulation of advanced aerospace vehicles, allowing people at remote locations to communicate more effectively and share information, increasing scientist's abilities to model the Earth's climate and forecast global environmental trends, and improving the development of advanced spacecraft. NASA's HPCC program is organized into three projects which are unique to the agency's mission: the Computational Aerosciences (CAS) project, the Earth and Space Sciences (ESS) project, and the Remote Exploration and Experimentation (REE) project. An additional project, the Basic Research and Human Resources (BRHR) project exists to promote long term research in computer science and engineering and to increase the pool of trained personnel in a variety of scientific disciplines. This document presents an overview of the objectives and organization of these projects as well as summaries of individual research and development programs within each project

Восьмая Сибирская конференция по параллельным и высокопроизводительным вычислениям : программа и тезисы докладов (28-30 октября 2015 года)

Представлены программа и тезисы докладов участников Восьмой Сибирской конференции по параллельным и высокопроизводительным вычислениям, которая пройдет в Томском государственном университете с 28 по 30 октября 2015 года при поддержке Министерства образования и науки РФ, Суперкомпьютерного кон-сорциума России, Российского фонда фундаментальных исследований (грант № 15-07-20872) и ЗАО «Intel Software». Для научных сотрудников, преподавателей, аспирантов, студентов, исполь-зующих высокопроизводительные вычислительные ресурсы в научной и учебной работе

Methods for Signal Filtering and Modelling and Their Parallel Distributed Computing Implementation

In this thesis the problem of filtering and modelling one-dimensional discrete signals and implementation of corresponding parallel distributed algorithms will be addressed. In Chapter 2, the research areas of parallel distributed computing environments, rank-based nonlinear filter and fractal functions are reviewed. In Chapter 3, an Interactive Parallel Distributed Computing Environment (IPDCE) is implemented based on Parallel Virtual Machine (PVM) and an interactive application development tool, the Tc1 language. The approach we use is to provide a Tc1 version interface for all procedures of the PVM interface library so that users can utilize any PVM procedure to do their parallel computing interactively. In Chapter 4, an interactive parallel stack-filtering system is implemented, based on the IPDCE. The user can play with this filtering system in both traditional command mode and modern Graphics User Interface (GUI) mode. In order to reduce the time required to compute a standard stack filter, a new minimum threshold decomposition scheme is introduced and other techniques such as minimizing the number of logical operations and utilizing the CPU bit-fields parallel property are also suggested. In this filtering system the user can select sequential or parallel stack-filtering algorithms. The parallel distributed stack-filtering algorithm is implemented with equal task partitioning and PVM. Two numerical simulations show that the interactive parallel stack-filtering system is efficient for both the sequential and the parallel filtering algorithms. In Chapter 5, an extended Iterated Function System (IFS) interpolation method is introduced for modelling a given discrete signal. In order to get the solution of the inverse IFS problem in reasonable time, a suboptimal search algorithm, which estimates first the local self-affine region and then the map parameters is suggested, and the neighbourhood information of a self-affine region is used for enhancing the robustness of this suboptimal algorithm. The parallel distributed version of the in-verse IFS algorithm is implemented with equal task partitioning and using a Remote Procedure Call application programming interface library. The numerical simulation results show that the IFS approach achieves a higher signal to noise ratio than does an existing approach based on autoregressive modelling for self-affine and approximately self-affine one-dimensional signals and, when the number of computers is small, the speed-up ratio is almost linear. In Chapter 6, inverse IFS interpolation is introduced to model self-affine and approximately self-affine one-dimensional signals corrupted by Gaussian noise. Local cross-validation is applied for compromising between the degree of smoothness and fidelity to the data. The parallel distributed version of the inverse algorithm is implemented in Parallel Virtual Machine (PVM) with static optimal task partitioning. A simple computing model is applied which partitions tasks based on only each computer's capability. Several numerical simulation results show that the new IFS inverse algorithm achieves a higher signal to noise ratio than does existing autoregressive modelling for noisy self-affine or approximately self-affine signals.- There is little machine idle time relative to computing time in the optimal task partition mode. In Chapter 7, local IFS interpolation, which realises the IFS limit for self-affine data, is applied to model non self-affi.ne signals. It is difficult, however, to explore the whole parameter space to achieve globally optimal parameter estimation. A two-stage search scheme is suggested to estimate the self-affine region and the associated region parameters so that a suboptimal solution can be obtained in reasonable time. In the first stage, we calculate the self-affine region under the condition that the associated region length is twice that of the self-affine region. Then the second stage calculates the associated region for each self-affine region using a full search space. In order to combat the performance degradation caused by the the difference of machines capabilities and unpredictable external loads, a dynamic load-balance technique based on a data parallelism scheme is applied in the parallel distributed version of the inverse local IFS algorithm. Some numerical simulations show that our inverse local IFS algorithm works efficiently for several types of one-dimensional signal, and the parallel version with dynamic load balance can automatically ensure that each machine is busy with computing and with low idle time

Large Model Visualization : Techniques and Applications

The size of datasets in scientific computing is rapidly increasing. This increase is caused by a boost of processing power in the past years, which in turn was invested in an increase of the accuracy and the size of the models. A similar trend enabled a significant improvement of medical scanners; more than 1000 slices of a resolution of 512x512 can be generated by modern scanners in daily practice. Even in computer-aided engineering typical models eas-ily contain several million polygons. Unfortunately, the data complexity is growing faster than the rendering performance of modern computer systems. This is not only due to the slower growing graphics performance of the graphics subsystems, but in particular because of the significantly slower growing memory bandwidth for the transfer of the geometry and image data from the main memory to the graphics accelerator. Large model visualization addresses this growing divide between data complexity and rendering performance. Most methods focus on the reduction of the geometric or pixel complexity, and hence also the memory bandwidth requirements are reduced. In this dissertation, we discuss new approaches from three different research areas. All approaches target at the reduction of the processing complexity to achieve an interactive visualization of large datasets. In the second part, we introduce applications of the presented ap-proaches. Specifically, we introduce the new VIVENDI system for the interactive virtual endoscopy and other applications from mechanical engineering, scientific computing, and architecture.The size of datasets in scientific computing is rapidly increasing. This increase is caused by a boost of processing power in the past years, which in turn was invested in an increase of the accuracy and the size of the models. A similar trend enabled a significant improvement of medical scanners; more than 1000 slices of a resolution of 512x512 can be generated by modern scanners in daily practice. Even in computer-aided engineering typical models eas-ily contain several million polygons. Unfortunately, the data complexity is growing faster than the rendering performance of modern computer systems. This is not only due to the slower growing graphics performance of the graphics subsystems, but in particular because of the significantly slower growing memory bandwidth for the transfer of the geometry and image data from the main memory to the graphics accelerator. Large model visualization addresses this growing divide between data complexity and rendering performance. Most methods focus on the reduction of the geometric or pixel complexity, and hence also the memory bandwidth requirements are reduced. In this dissertation, we discuss new approaches from three different research areas. All approaches target at the reduction of the processing complexity to achieve an interactive visualization of large datasets. In the second part, we introduce applications of the presented ap-proaches. Specifically, we introduce the new VIVENDI system for the interactive virtual endoscopy and other applications from mechanical engineering, scientific computing, and architecture

Graduate Catalog, 1999-2002, New Jersey Institute of Technology

https://digitalcommons.njit.edu/coursecatalogs/1004/thumbnail.jp