Publications Details

ALAMO: Autonomous lightweight allocation, management, and optimization

Brightwell, Ronald B.; Ferreira, Kurt B.; Grant, Ryan E.; Levy, Scott L.; Lofstead, Gerald F.; Olivier, Stephen L.; Laros, James H.; Younge, Andrew J.; Gentile, Ann C.; Laros, James H.

Several recent workshops conducted by the DOE Advanced Scientific Computing Research program have established the fact that the complexity of developing applications and executing them on high-performance computing (HPC) systems is rising at a rate which will make it nearly impossible to continue to achieve higher levels of performance and scalability. Absent an alternative approach to managing this ever-growing complexity, HPC systems will become increasingly difficult to use. A more holistic approach to designing and developing applications and managing system resources is required. This paper outlines a research strategy for managing the increasing the complexity by providing the programming environment, software stack, and hardware capabilities needed for autonomous resource management of HPC systems. Developing portable applications for a variety of HPC systems of varying scale requires a paradigm shift from the current approach, where applications are painstakingly mapped to individual machine resources, to an approach where machine resources are automatically mapped and optimized to applications as they execute. Achieving such automated resource management for HPC systems is a daunting challenge that requires significant sustained investment in exploring new approaches and novel capabilities in software and hardware that span the spectrum from programming systems to device-level mechanisms. This paper provides an overview of the functionality needed to enable autonomous resource management and optimization and describes the components currently being explored at Sandia National Laboratories to help support this capability.