Experiences with Target-Platform Heterogeneity in Clouds, Grids, and On-Premises Resources

Heterogeneity in secondary characteristics of different HPC target platforms is the focus of this paper. Clusters, grids, and (IaaS) clouds may appear straightforward to configure to be interchangeable - but our experiences with mainstream parallel codes for CFD demonstrate that secondary attributes - support software, interconnect type, availability, access, and cost - expose heterogeneous aspects that impact overall effectiveness of application execution. The emergence of clouds as alternatives to grids and local resources for parallel HPC codes portends "computing as a utility" in science and engineering domains. Our experiences provide preliminary insights into characterizing these different types of platforms to which users typically have access - and show where the tradeoffs can be, in terms of deployment effort, actual and nominal costs, application performance, and availability (both in terms of resource size and time to gain access). For our test application, we report that each of the platforms to which we had access had its particular benefits and drawbacks in terms of the above attributes. More generally, our experiences may provide an example preview into what developers and users can expect when selecting a "utility provider" and specific instance thereof for a particular run of their application.