Parallel solution of sparse one-dimensional dynamic programming problems

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Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, National Technical Information Service, distributor , Hampton, VA, [Springfield, Va
Computer programming., Computer techniques., Multiprocessing (Computers), Parallel program
Other titlesParallel solution of sparse one dimensional dynamic programming problems.
StatementDavid M. Nicol.
SeriesICASE report -- no. 89-17., NASA contractor report -- 181812., NASA contractor report -- NASA CR-181812.
ContributionsInstitute for Computer Applications in Science and Engineering.
The Physical Object
FormatMicroform
Pagination1 v.
ID Numbers
Open LibraryOL15407954M

Get this from a library. Parallel solution of sparse one-dimensional dynamic programming problems. [David M Nicol; Institute for Computer Applications in Science and Engineering.]. Introducation to Parallel Computing is a complete end-to-end source of information on almost all aspects of parallel computing from introduction to architectures to programming paradigms to algorithms to programming standards.

It is the only book to have complete coverage of traditional Computer Science algorithms (sorting, graph and matrix. We present the underlying problems, the solution algorithms, and the parallel implementation strategies.

Smart load-balancing, partitioning, and ordering techniques are used to enhance parallel. The most important thing for the dynamic programming pattern is that Parallel solution of sparse one-dimensional dynamic programming problems book should prove that the solution of the higher‐level problem expressed in optimal solutions of the sub‐ problems is optimal.

This part might be tough; if you can’t figure out a recursive relation, try the divide‐and‐conquer pattern or. Sparse Dynamic Programming. January ; DOI: due to natural constraints of the problems, only a sparse set matters for the result.

Based on new algorithmic techniques, we obtain algorithms. TutORials in OR Book Series; Topics in OR Book Series ; Editor's Cut; ICYMI; Pricing & Subscriptions ; Parallel Solution of Sparse One-Dimensional Dynamic Programming Problems.

David M. Nicol. Pages: – Published Online: Using Simulated Annealing to Solve Controlled Rounding Problems. James Kelly, Bruce Golden, Arjang Assad.

A new method is presented for distributing data in sparse matrix-vector multiplication. The method is two-dimensional, tries to minimize the true communication volume, and also tries to spread the computation and communication work evenly over the processors. The method starts with a recursive bipartitioning of the sparse matrix, each time splitting a rectangular matrix into two parts with a Cited by: The Use of Vector and Parallel Computers in the Solution of Large Sparse Linear Equations.

Large Scale Scientific Computing, () Iterative Methods for the Solution of Elliptic Problems on Regions Partitioned into by: Other large dynamic programming problems without an obvious geometric interpretation may also be solvable by our iterative approach.

REFERENCES 1. L.A. Hageman and D.M. Young, Applied Iterative Methods, Academic Press, New York, (). Angel and R. Bellman, Dynamic Programming and Partial Differential Equations, Academic Press, New York Cited by: 3.

The data parallel method asks for less programming effort and takes many problems (such as dead-lock), which are typical for a parallel machine, out of the hands of the programmer.

Details Parallel solution of sparse one-dimensional dynamic programming problems EPUB

Thus, this method is the preferred one, since the step from a vector program to a parallel program is smaller.

Tree DP Example Problem: given a tree, color nodes black as many as possible without coloring two adjacent nodes Subproblems: – First, we arbitrarily decide the root node r – B v: the optimal solution for a subtree having v as the root, where we color v black – W v: the optimal solution for a subtree having v as the root, where we don’t color v – Answer is max{B.

Short answer: To do dynamic programming, you find out which subcomputations are performed more than once and instead perform them only once, storing their outputs. The outputs are stored in a table of some sort.

In general, you can think of that. Sparse Matrix Partitioning for Parallel Eigenanalysis of Large Static and Dynamic Graphs on speeding up the solution of the eigen system, Bx i = ix i, where B= A E[A] is the residual matrix. For the purpose of to solve these very large graph problems.

One-Dimensional Partitioning Initially, we explored the performance of the. Chapter 32 of this book discusses important limitations and errors associated with using floating-point rather than integer addresses, and those problems apply to the techniques presented in this section.

1D Arrays. One-dimensional arrays are represented by packing the data into a. Question: Using One Dimensional Parallel Arrays.(C++ Program) Create Two Arrays With 5 Elements Each: One Will Hold Strings And The Second Will Hold Integers.

Write A Program To Ask The User To Enter 5 Student Names And Their Ages. Output The Data From The Parallel Arrays. Sample Run: Your Program Must Run Exactly Like The Example Below. In numerical analysis and scientific computing, a sparse matrix or sparse array is a matrix in which most of the elements are zero.

By contrast, if most of the elements are nonzero, then the matrix is considered number of zero-valued elements divided by the total number of elements (e.g., m × n for an m × n matrix) is called the sparsity of the matrix (which is equal to 1 minus the.

Speedup Anomalies in Parallel Search Algorithms Analysis of Average Speedup in Parallel DFS Bibliographic Remarks Problems CHAPTER 12 Dynamic Programming Overview of Dynamic Programming Serial Monadic DP Formulations The Shortest-Path Problem The 0/1 Knapsack Problem Section 3 presents parallel algorithms for SpGEMM.

We propose novel algorithms based on 2D block decomposition of data in addition to giving the complete description of an existing 1D algorithm. To the best of our knowledge, parallel algorithms using a 2D block decomposition have not earlier been developed for sparse matrix-matrix by: Abstract.

It is known that many problems which can be solved sequentially by dynamic programming, are in the class most of these problems takes O(log 2 n) time on parallel models like CREW PRAM, but the number of processors involved is usually a high degree polynomial and the total work (i.e., the processor-time product) is very unfavorable in comparing with the work (i.e., the time Cited by: 5.

This third book in a suite of four practical guides is an engineers companion to using numerical methods for the solution of complex mathematical problems. The required software is provided by way of the freeware mathematical library BzzMath that is developed and maintained by the authors.

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The present volume focuses on optimization and nonlinear systems solution. The book describes numerical. The Future: During the past 20+ years, the trends indicated by ever faster networks, distributed systems, and multi-processor computer architectures (even at the desktop level) clearly show that parallelism is the future of computing.

In this same time period, there has been a greater than ,x increase in supercomputer performance, with no end currently in sight. B Dynamic Mapping Distribute the work among processes during the execution of the algorithm.

If tasks are generated dynamically, then they must be mapped dynamically too. If the amount of data associated with tasks is large relative to the computation, then a dynamic mapping may entail moving this data among processes.

In a shared-address. Although for the most part limited to the one-dimensional case, our results demonstrate the potential of parallel computing for this class of problems.

It is found that the efficiency of the proposed algorithms increases as the problem size by: 1. n any type of power system analyses, a sparse linear solver to solve matrix equation such as inversion is the core part of such analysis.

Without this solver, power system analyses cantlot be solved.

Description Parallel solution of sparse one-dimensional dynamic programming problems PDF

The solver will determine the accuracy of the solution and also the analysis solution time, whether slow or. Parallel programming. crumb trail: > parallel > Parallel programming. Parallel programming is more complicated than sequential programming. While for sequential programming most programming languages operate on similar principles (some exceptions such as functional or logic languages aside), there is a variety of ways of tackling parallelism.

Overview. This collection of over 40 successful parallel applications is woven into a discussion of other key features of HPCC: History of Parallel Computing as pertained to work at Caltech, Chapter 2.; A survey of the evolution of parallel machines; the link: ` Survey of HPCC' contains further updated resources.

Computer Architectures are not not directly discussed in Parallel Computing Works. This third book in a suite of four practical guides is an engineers companion to using numerical methods for the solution of complex mathematical problems.

The required software is provided by way of the freeware mathematical library BzzMath that is developed and maintained by the authors. The present volume focuses on optimization and nonlinear systems solution.

The challenges in working with the Geometric Decomposition pattern are best appreciated in the low-level details of the resulting programs. Therefore, even though the techniques used in these programs are not fully developed until much later in the book, we provide full programs in this section rather than high-level descriptions of the solutions.

The CLSOCP package provides an implementation of a one-step smoothing Newton method for the solution of second order cone programming (SOCP) problems. CSDP is a library of routines that implements a primal-dual barrier method for solving semidefinite programming problems; it is interfaced in the Rcsdp package.

Solving problems on concurrent processors is a long-awaited outgrowth of the extensive experience at the California Institute of Technology in the field of solving scientific problems on message-passing parallel processors.

The book shows how many standard mathematical operations can be distributed, including iterative relaxation methods. Fortran Aware Editors: Emacs - Editor Macros (LISP) - GNU Emacs FAQ - Fortran 90 Free-Format Mode Code (Make Emacs F90 Aware): PFE - a large-capacity, multi-file editor that runs on Wind Wind Windows NT and Windows on Intel-compatible processors, and on Windows x.

VI - General purpose text editor available for DOS, WIN16, WIN32, OS/2, VMS, Mac, Atari, Amiga. Springs play a fundamental role in legged locomotion. In nature, elastic elements are used for energy storage, as return springs, and to cushion impacts [].Model-based analyses have shown that compliant legs can explain the dynamics of human walking and running [], as well as a wide variety of quadrupedal gaits, including walking, trotting, tölting, and galloping [3,4].Cited by: 4.Parallel Computing Works This book describes work done at the Caltech Concurrent Computation Program, Pasadena, Califonia.

This project ended in but .