Elastic execution of machine learning workloads using application based profiling

US11429434B2 · kind B2 · utility

Assignee

• International Business Machines Corporation · US

Inventors

• Liana L. Fong · Dobbs Ferry, US
• Seetharami R. Seelam · Yorktown Heights, US
• Ganesh Venkataraman · Rocky Hill, US
• Debashish Saha · White Plains, US
• Punleuk Oum · Tarrytown, US
• Archit Verma · New York, US
• Prabhat Maddikunta Reddy · Danbury, US

Key dates

• Filing date: Dec 23, 2019
• Grant date: Aug 30, 2022
• Expiry date: Jul 27, 2040

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N5/02
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

Embodiments relate to a system, program product, and method for supporting elastic execution of a machine learning (ML) workload using application based profiling. A joint profile comprised of both ML application execution and resource usage data is generated. One or more feature(s) and signature(s) from the joint profile are identified, and a ML execution model for ML application execution and resource usage is built. The ML execution model leverages the feature(s) and signature(s) and is applied to provide one or more directives to subsequent application execution. The application of the ML execution model supports and enables the ML execution to elastically allocate and request one or more resources from a resource management component, with the elastic allocation supporting application execution.