A2L2

High-performance computing (HPC) is focused on providing large-scale compute capacity to scientific applications. HPC schedulers tend to be optimized for large parallel batch jobs and, as such, often overlook the requirements of other scientific applications. In this work, we propose a cloud-inspired HPC scheduling model that aims to capture application performance and requirement models (Application Aware - A2) and dynamically resize malleable application resource allocations to be able to support applications with critical performance or deadline requirements. (Low Latency allocation - L2). The proposed model incorporates measures to improve data-intensive applications performance on HPC systems and is derived from a set of cloud scheduling techniques that are identified as applicable in HPC environments. The model places special focus on dynamically malleable applications; data-intensive applications that support dynamic resource allocation without incurring severe performance penalties; which are proposed for fine-grained backfilling and dynamic resource allocation control without job preemption.