Data Placement Analysis For A Distributed Heterogeneous . . .

Heterogeneous Associative Computing (HAsC) is a new distributed heterogeneous computing paradigm that is a combination of associative computing, superconcurrency, and distributed heterogeneous computing. Its task mapping and resulting execution decision is based on data locality along with a variety of system, data, and algorithmic factors. In this paper, we present Data Placement Analysis (DPA) that is an effective and efficient method of placing the right data set on suitable machines for execution. Thus, when incorporated into HAsC task mapping, DPA will strongly influence the machine selection / task mapping process. The effectiveness of DPA is then shown with experimental results. 1.0 Introduction Distributed heterogeneous high performance computing, also called metacomputing (Smarr and Callett 1992), is where a single program's tasks are distributed among a network of heterogeneous machines. Although this network of machines is viewed by the user's program as a single unified v..