Revised Pursuit Algorithm for Solving Non-stationary Linear Programming Problems on Modern Computing Clusters with Manycore Accelerators

Sokolinskaya, Irina M.; Sokolinsky, Leonid B.

doi:10.1007/978-3-319-55669-7_17

Cited by 21 publications

(17 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…com/leonid-sokolinsky/BSF-Cimmino. The system of inequalities was taken from the model scalable linear-programming problem Model-n given in [24]. In this system, the number of inequalities is m = 2n + 2, where n is the dimension of the space.…”

Section: Numerical Experimentsmentioning

confidence: 99%

Scalability Evaluation of Cimmino Algorithm for Solving Linear Inequality Systems on Multiprocessors with Distributed Memory

Sokolinsky

Sokolinskaya

2018

JSFI

View full text Add to dashboard Cite

The paper is devoted to a scalability study of Cimmino algorithm for linear inequality systems. This algorithm belongs to the class of iterative projection algorithms. For the analytical analysis of the scalability, the BSF (Bulk Synchronous Farm) parallel computation model is used. An implementation of the Cimmino algorithm in the form of operations on lists using higher-order functions Map and Reduce is presented. An analytical estimation of the upper scalability bound of the algorithm for cluster computing systems is derived. An information about the implementation of Cimmino algorithm on lists in C++ language using the BSF program skeleton and MPI parallel programming library is given. The results of large-scale computational experiments performed on a cluster computing system are demonstrated. A conclusion about the adequacy of the analytical estimations by comparing them with the results of computational experiments is made.

show abstract

Section: Numerical Experimentsmentioning

confidence: 99%

Scalability Evaluation of Cimmino Algorithm for Solving Linear Inequality Systems on Multiprocessors with Distributed Memory

Sokolinsky

Sokolinskaya

2018

JSFI

View full text Add to dashboard Cite

show abstract

“…Based on this implementation, we calculate the time complexity of one iteration and give an analytical estimation of the scalability upper bound of the NSLP algorithm in the Targeting phase. For calculating, we use the synthetic scalable linear programming problem of the dimension called Model-n [17]. This LP problem has the matrix A of the size n × 2(n + 1).…”

Section: Bsf-implementation Of Nslp Algorithmmentioning

confidence: 99%

“…The implementation of the Qwest phase was described and evaluated by us in the paper [17]. In the present work, we have done the implementation of the Targeting phase in C language using the BSF skeleton.…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…The source code of this program is freely available on Github, at https://github.com/leonid-sokolinsky/BSF-NSLP. We investigated the speedup and parallel efficiency of this BSF-program on the supercomputer "Tornado SUSU" [23] using the synthetic scalable linear programming problem Model n [17] mentioned in the section 4. The calculations were performed for the dimensions 400, 800 and 1080.…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…The Quest phase was investigated in [12], where the convergence theorem was proved for the case when the polytope is translated with a fixed vector in the each unit of time. In the paper [17], the authors demonstrated that Intel Xeon Phi multi-core processors can be efficiently used for calculating the pseudo-projections.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Scalability Evaluation of NSLP Algorithm for Solving Non-Stationary Linear Programming Problems on Cluster Computing Systems

Sokolinskaya

Sokolinsky

2017

Communications in Computer and Information Science

Self Cite

View full text Add to dashboard Cite

The paper is devoted to a scalability study of the NSLP algorithm for solving non-stationary high-dimension linear programming problem on the cluster computing systems. The analysis is based on the BSF model of parallel computations. The BSF model is a new parallel computation model designed on the basis of BSP and SPMD models. The brief descriptions of the NSLP algorithm and the BSF model are given. The NSLP algorithm implementation in the form of a BSF program is considered. On the basis of the BSF cost metric, the upper bound of the NSLP algorithm scalability is derived and its parallel efficiency is estimated. NSLP algorithm implementation using BSF skeleton is described. A comparison of scalability estimations obtained analytically and experimentally is provided.Keywords: non-stationary linear programming problem · large-scale linear programming · NSLP algorithm · BSF parallel computation model · cost metric · scalability bound · parallel efficiency estimation.

show abstract

Scalable Algorithm for Subsequence Similarity Search in Very Large Time Series Data on Cluster of Phi KNL

Kraeva¹,

Zymbler²

2019

Communications in Computer and Information Science

View full text Add to dashboard Cite

Revised Pursuit Algorithm for Solving Non-stationary Linear Programming Problems on Modern Computing Clusters with Manycore Accelerators

Cited by 21 publications

References 1 publication

Scalability Evaluation of Cimmino Algorithm for Solving Linear Inequality Systems on Multiprocessors with Distributed Memory

Scalability Evaluation of Cimmino Algorithm for Solving Linear Inequality Systems on Multiprocessors with Distributed Memory

Scalability Evaluation of NSLP Algorithm for Solving Non-Stationary Linear Programming Problems on Cluster Computing Systems

Scalable Algorithm for Subsequence Similarity Search in Very Large Time Series Data on Cluster of Phi KNL

Contact Info

Product

Resources

About