Selecting Efficient Cloud Resources for HPC Workloads

Constant advances in CPU, storage, and network virtualization are enabling high-performance computing (HPC) applications to be efficiently executed on cloud computing systems. In this computing model, users pay only for what they use, with no need to acquire nor maintain expensive computing infrastructure. Moreover, users have at their disposal multiple kinds of computing resources and are able to assemble computing infrastructures that fit the application needs. Nonetheless, the available computing resources vary in price and performance and selecting the proper resources to execute the applications is of utmost importance to optimize cost and performance. In this work, we discuss the performance and cost implications of selecting different kinds of cloud resources to execute HPC workloads and show that the best resources for executing a given application depend not only on the application itself but also on the input dataset being processed. We also propose a methodology to support the selection of efficient cloud resources for these applications and show that is was able to select the best of 11 different cloud infrastructure configurations to execute 8 different benchmarks by executing just a few seconds of each application on each one of the configurations.