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

TL;DR

This paper evaluates the scalability of the NSLP algorithm for high-dimensional non-stationary linear programming on cluster systems using the BSF parallel computation model, providing both theoretical and experimental analysis.

Contribution

It introduces a new scalability analysis framework for the NSLP algorithm based on the BSF model, combining theoretical bounds with experimental validation.

Findings

Upper bound of NSLP scalability derived from BSF cost metric

Parallel efficiency estimated and compared with experimental results

BSF model effectively predicts algorithm scalability on cluster systems

Abstract

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.