Scalability Evaluation of NSLP Algorithm for Solving Non-Stationary Linear Programming Problems on Cluster Computing Systems
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). First posted on arXiv 14 Sep 2017.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 1.
1 paper.
- 2022Online Regenerative Learningcited by 0
- 2022Online Regenerative Learning18 Sep 2022