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Report presents parallel algorithms and special parallel architecture for computation of forward dynamics of robotics manipulators. Products of effort to find best method of parallel computation to achieve required computational efficiency. Significant speedup of computation anticipated as well as cost reduction.
Parallel structural methods, research team activities, advanced architecture computers for parallel computational structural mechanics (CSM) research, the FLEX/32 multicomputer, a parallel structural analyses testbed, blade-stiffened aluminum panel with a circular cutout and the dynamic characteristics of a 60 meter, 54-bay, 3-longeron deployable truss beam are among the topics discussed.
A parallel architecture for computation of the linear convolution of two sequences of arbitrary lengths using the Fermat number transform (FNT) is described. In particular a pipeline structure is designed to compute a 128-point FNT. In this FNT, only additions and bit rotations are required. A standard barrel shifter circuit is modified so that it performs the required bit rotation operation. The overlap-save method is generalized for the FNT to compute a linear convolution of arbitrary length. A parallel architecture is developed to realize this type of overlap-save method using one FNT and several inverse FNTs of 128 points. The generalized overlap save method alleviates the usual dynamic range limitation in FNTs of long transform lengths. Its architecture is regular, simple, and expandable, and therefore naturally suitable for VLSI implementation.
While parallel computers offer significant computational performance, it is generally necessary to evaluate several programming strategies. Two programming strategies for a fairly common problem - a periodic tridiagonal solver - are developed and evaluated. Simple model calculations as well as timing results are presented to evaluate the various strategies. The particular tridiagonal solver evaluated is used in many computational fluid dynamic simulation codes. The feature that makes this algorithm unique is that these simulation codes usually require simultaneous solutions for multiple right-hand-sides (RHS) of the system of equations. Each RHS solutions is independent and thus can be computed in parallel. Thus a Gaussian elimination type algorithm can be used in a parallel computation and the more complicated approaches such as cyclic reduction are not required. The two strategies are a transpose strategy and a distributed solver strategy. For the transpose strategy, the data is moved so that a subset of all the RHS problems is solved on each of the several processors. This usually requires significant data movement between processor memories across a network. The second strategy attempts to have the algorithm allow the data across processor boundaries in a chained manner. This usually requires significantly less data movement. An approach to accomplish this second strategy in a near-perfect load-balanced manner is developed. In addition, an algorithm will be shown to directly transform a sequential Gaussian elimination type algorithm into the parallel chained, load-balanced algorithm.
This paper is concerned with the implementation of a 3D unstructured-grid Euler-solver on massively parallel distributed-memory computer architectures. The goal is to minimize solution time by achieving high computational rates with a numerically efficient algorithm. An unstructured multigrid algorithm with an edge-based data-structure has been adopted, and a number of optimizations have been devised and implemented in order to accelerate the parallel computational rates. The implementation is carried out by creating a set of software tools, which ease the implementation of computational problems on parallel architecture machines by relieving the user of the low-level machine specific issues. The quantitative effect of the various optimizations are demonstrated, and we show that the combined effect of these optimizations leads to roughly a factor of three performance improvement. The overall solution efficiency is compared with that obtained on the CRAY-YMP vector supercomputer.
Digital optical computer design has been focused primarily towards parallel (single point-to-point interconnection) implementation. This architecture is compared to currently developing VHSIC systems. Using demonstrated multichannel acousto-optic devices, a figure of merit can be formulated. The focus is on a figure of merit termed Gate Interconnect Bandwidth Product (GIBP). Conventional parallel optical digital computer architecture demonstrates only marginal competitiveness at best when compared to projected semiconductor implements. Global, analog global, quasi-digital, and full digital interconnects are briefly examined as alternative to parallel digital computer architecture. Digital optical computing is becoming a very tough competitor to semiconductor technology since it can support a very high degree of three dimensional interconnect density and high degrees of Fan-In without capacitive loading effects at very low power consumption levels.
Fundamental problems encountered while attempting to develop automated techniques for applications of remote sensing are discussed under the following categories: (1) geometric and radiometric preprocessing; (2) spatial, spectral, temporal, syntactic, and ancillary digital image representation; (3) image partitioning, proportion estimation, and error models in object scene interference; (4) parallel processing and image data structures; and (5) continuing studies in polarization; computer architectures and parallel processing; and the applicability of "expert systems" to interactive analysis.
Programming languages, computer graphics, neural networks, massively parallel computers, SIMD architecture, algorithms, digital terrain models, sort computation, simulation of charged particle transport on the massively parallel processor and image processing are among the topics discussed.
Recently, networked and cluster computation have become very popular. This paper is an introduction to a new C based parallel language for architecture-adaptive programming, aCe C. The primary purpose of aCe (Architecture-adaptive Computing Environment) is to encourage programmers to implement applications on parallel architectures by providing them the assurance that future architectures will be able to run their applications with a minimum of modification. A secondary purpose is to encourage computer architects to develop new types of architectures by providing an easily implemented software development environment and a library of test applications. This new language should be an ideal tool to teach parallel programming. In this paper, we will focus on some fundamental features of aCe C.
In this paper the implementation of a parallel O(LogN) algorithm for computation of rigid multibody dynamics on a Hypercube MIMD parallel architecture is presented. To our knowledge, this is the first algorithm that achieves the time lower bound of O(LogN) by using an optimal number of O(N) processors. However, in addition to its theoretical significance, the algorithm is also highly efficient for practical implementation on commercially available MIMD parallel architectures due to its highly coarse grain size and simple communication and synchronization requirements. We present a multilevel parallel computation strategy for implementation of the algorithm on a Hypercube. This strategy allows the exploitation of parallelism at several computational levels as well as maximum overlapping of computation and communication to increase the performance of parallel computation.
In this paper the implementation of a parallel O (Log N) algorithm for computation of rigid multibody dynamics on a Hypercube MIMD parallel architecture is presented.
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This viewgraph presentation discusses unsteady flow simulations for a turbopump intended for a reusable launch vehicle (RLV). The simulation process makes use of computational grids and parallel processing. The architecture of the parallel computers used is discussed, as is the scripting of turbopump simulations.
Goodyear Aerospace delivered the Massively Parallel Processor (MPP) to NASA/Goddard in May 1983, over three years ago. Ever since then, Goodyear has tried to look in a forward direction. There is always some debate as to which way is forward when it comes to supercomputer architecture. Improvements to the MPP's massively parallel architecture are discussed in the areas of data I/O, memory capacity, connectivity, and indirect (or local) addressing. In I/O, transfer rates up to 640 megabytes per second can be achieved. There are devices that can supply the data and accept it at this rate. The memory capacity can be increased up to 128 megabytes in the ARU and over a gigabyte in the staging memory. For connectivity, there are several different kinds of multistage networks that should be considered.
The implementation of certain algorithms on parallel processing computing architectures can involve partitioning contiguous elements into a fixed number of groups, each of which is to be handled by a single processor. It is desired to find an assignment of elements to processors that minimizes the sum of the maximum workloads experienced at each stage. This problem can be viewed as a multi-objective network optimization problem. Polynomially-bounded algorithms are developed for the case of two stages, whereas the associated decision problem (for an arbitrary number of stages) is shown to be NP-complete. Heuristic procedures are therefore proposed and analyzed for the general problem. Computational experience with one of the exact problems, incorporating certain pruning rules, is presented with one of the exact problems. Empirical results also demonstrate that one of the heuristic procedures is especially effective in practice.
A high-resolution finite-volume General Circulation Model (fvGCM), resulting from a development effort of more than ten years, is now being run operationally at the NASA Goddard Space Flight Center and Ames Research Center. The model is based on a finite-volume dynamical core with terrain-following Lagrangian control-volume discretization and performs efficiently on massive parallel architectures. The computational efficiency allows simulations at a resolution of a quarter of a degree, which is double the resolution currently adopted by most global models in operational weather centers. Such fine global resolution brings us closer to overcoming a fundamental barrier in global atmospheric modeling for both weather and climate, because tropical cyclones and even tropical convective clusters can be more realistically represented. In this work, preliminary results of the fvGCM are shown. Fifteen simulations of four Atlantic tropical cyclones in 2002 and 2004 are chosen because of strong and varied difficulties presented to numerical weather forecasting. It is shown that the fvGCM, run at the resolution of a quarter of a degree, can produce very good forecasts of these tropical systems, adequately resolving problems like erratic track, abrupt recurvature, intense extratropical transition, multiple landfall and reintensification, and interaction among vortices.
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A parallel-processing computer program finds a few eigenvalues in a sparse Hermitian matrix that contains as many as 100 million diagonal elements. This program finds the eigenvalues faster, using less memory, than do other, comparable eigensolver programs. This program implements a Lanczos algorithm in the American National Standards Institute/ International Organization for Standardization (ANSI/ISO) C computing language, using the Message Passing Interface (MPI) standard to complement an eigensolver in PARPACK. [PARPACK (Parallel Arnoldi Package) is an extension, to parallel-processing computer architectures, of ARPACK (Arnoldi Package), which is a collection of Fortran 77 subroutines that solve large-scale eigenvalue problems.] The eigensolver runs on Beowulf clusters of computers at the Jet Propulsion Laboratory (JPL).