Concurrent adaptive computing in heterogeneous environments (CACHE)

Abstract

We introduce a computational framework for concurrent adaptive computing in heterogeneous environments for computationally intensive applications. This framework considers the presence of inter-connected computational resources which are discoverable and a workload which needs to be executed either by concurrent means or on a singular resource. The selection of resources, using a novel measurement of performance, leads to the adaptive inclusion/exclusion of resources to be used in the efficient execution of workload computations. The adaptive approach is that it makes a determination to include a proper subset of resources for system inclusion to execute a workload, which is contrary (except when the subset is allinclusive) to a greedy approach where all the resources are seized for the workload application. The selection of a subset of resources may be more efficient due to the high level of heterogeneity of the resources, where, for some resources, certain resource selections may be detrimental or have no value to send work there. Furthermore, this framework aims to lessen the unpredictability and uncontrollability of heterogeneous systems by using this analysis for resource selection.