Analytical Estimation of the Scalability of Iterative Numerical Algorithms on Distributed Memory Multiprocessors

TL;DR

This paper introduces the BSF parallel computational model, extending BSP, to predict the scalability and efficiency of iterative numerical algorithms on distributed-memory multiprocessors.

Contribution

The paper presents the BSF model, a new high-level framework for analyzing and estimating the scalability of iterative numerical algorithms on distributed systems.

Findings

BSF model accurately predicts upper scalability bounds.

Provides equations for speedup and efficiency estimation.

Extends BSP to better suit iterative numerical methods.

Abstract

This article presents a new high-level parallel computational model named BSF - Bulk Synchronous Farm. The BSF model extends the BSP model to deal with the compute-intensive iterative numerical methods executed on distributed-memory multiprocessor systems. The BSF model is based on the master-worker paradigm and the SPMD programming model. The BSF model makes it possible to predict the upper scalability bound of a BSF-program with great accuracy. The BSF model also provides equations for estimating the speedup and parallel efficiency of a BSF-program.