A taxonomy for describing matching and scheduling heuristics for mixed-machine heterogeneous computing systems

The problem of mapping (defined as matching and scheduling) tasks and communications onto multiple machines and networks an a heterogeneous computing (HC) environment has been shown to be NP-complete, In general, requiring the development of heuristic techniques. Many different types of mapping heuristics have been developed in recent years. However, selecting the best heuristic to use in any given scenario remains a difficult problem. Factors making this selection difficult are discussed. Motivated by these difficulties, a new taxonomy for classifying mapping heuristics for HC environments as proposed ("the Purdue HC Taxonomy"). The taxonomy is defined in three major parts: (1) the models used for applications and communication requests, (2) the models used for target hardware platforms, and (3) the characteristics of mapping heuristics. Each part of the taxonomy as described, with examples given to help clarify the taxonomy. The benefits and uses of this taxonomy are also discussed.