Journal Article10.1007/BF01299446
Data parallel computing for network-structured optimization problems
10
TL;DR: Practical approaches to the data-parallel solution of large scale optimization problems with network—or embedded-network—structures and empirical results that highlight the performance of the algorithms on a data-Parallel computer, the Connection Machine CM-2 are discussed.
read more
Abstract: Data level parallelism is a type of parallelism whereby operations are performed on many data elements concurrently, by many processors. These operations are (more or less) identical, and are executed in a synchronous, orderly fashion. This type of parallelism is used by massively parallel SIMD (i.e., Single Instruction, Multiple Data) architectures, like the Connection Machine CM-2, the AMT DAP and Masspar, and MIMD (i.e., Multiple Instruction, Multiple Data) architectures, like the Connection Machine CM-5. Data parallelism can also be described by a theoretical model of computation: the Vector-Random Access Machine (V-RAM).
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Robust Optimization of Large-Scale Systems
TL;DR: This paper characterize the desirable properties of a solution to models, when the problem data are described by a set of scenarios for their value, instead of using point estimates, and develops a general model formulation, called robust optimization RO, that explicitly incorporates the conflicting objectives of solution and model robustness.
2K
Data-Parallel Implementations of Dense Simplex Methods on the Connection Machine CM-2
TL;DR: Three data-parallel implementations of the simplex method for dense linear programming problems are described, using a full tableau and the most-negative reduced cost pivot rule and a revised method with explicit inverse.
31
Symmetric and Asymmetric Parallelization of a Cost-Decomposition Algorithm for Multicommodity Flow Problems
TL;DR: This work shows how to exploit a common characteristic of current supercomputer facilities, i.e., the side-to-side availability of massively parallel and vector supercomputers, to implement an asymmetric decomposition algorithm where each architecture is used for the tasks for which it is best suited.
A block-parallel conjugate gradient method for separable quadratic programming problems^1
Eiki Yamakawa,Masao Fukusima +1 more
TL;DR: This paper considers a block-parallel modification of the conjugate gradient method, which is suitable for implementation on a parallel computer and concludes that the proposed method is effective particularly for problems with some block structure.
9
A data parallel augmenting path algorithm for the dense linear many-to-one assignment problem
TL;DR: A data parallel primal-dual augmenting path algorithm for the dense linear many-to-one assignment problem also known as semi-assignment is described and it is shown that the best known sequential computational complexity of O(mn2) for dense problems, is reduced to the parallel complexity ofO(mn), on a machine with n processors supporting reductions in O(1) time.
5
References
•Book
Parallel and Distributed Computation: Numerical Methods
Dimitri P. Bertsekas,John N. Tsitsiklis +1 more
- 01 Jan 1989
TL;DR: This work discusses parallel and distributed architectures, complexity measures, and communication and synchronization issues, and it presents both Jacobi and Gauss-Seidel iterations, which serve as algorithms of reference for many of the computational approaches addressed later.
7K
Validity of the single processor approach to achieving large scale computing capabilities
Gene Myron Amdahl
- 18 Apr 1967
TL;DR: In this paper, the authors argue that the organization of a single computer has reached its limits and that truly significant advances can be made only by interconnection of a multiplicity of computers in such a manner as to permit cooperative solution.
4K
Monotone Operators and the Proximal Point Algorithm
TL;DR: In this paper, the proximal point algorithm in exact form is investigated in a more general form where the requirement for exact minimization at each iteration is weakened, and the subdifferential $\partial f$ is replaced by an arbitrary maximal monotone operator T.
3.9K
Partitioning procedures for solving mixed-variables programming problems
TL;DR: In this article, the 8th International Meeting of the Institute of Management Sciences, Brussels, August 23-26, 1961, the authors presented a paper entitled "The International Journal of Management Science and Management Sciences".
3.5K
On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators
TL;DR: This paper shows, by means of an operator called asplitting operator, that the Douglas—Rachford splitting method for finding a zero of the sum of two monotone operators is a special case of the proximal point algorithm, which allows the unification and generalization of a variety of convex programming algorithms.