18094028. APPLICATION EXECUTION ALLOCATION USING MACHINE LEARNING simplified abstract (NVIDIA Corporation)
Contents
APPLICATION EXECUTION ALLOCATION USING MACHINE LEARNING
Organization Name
Inventor(s)
Ronald N. Isaac of San Ramon CA (US)
APPLICATION EXECUTION ALLOCATION USING MACHINE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18094028 titled 'APPLICATION EXECUTION ALLOCATION USING MACHINE LEARNING
The patent application describes apparatuses, systems, and techniques for assigning the execution of applications to different processing units using machine learning.
- Identification of usage data for an application to be executed on a computing system with both an integrated processing unit and a discrete processing unit.
- Allocation of operations of the application to either the integrated processing unit or the discrete processing unit based on the identified usage data and system performance metrics or user experience metrics.
- Optimization of application execution by dynamically assigning tasks to the most suitable processing unit based on real-time data and metrics.
Potential Applications: - This technology can be applied in mobile devices to enhance performance and user experience. - It can be used in cloud computing environments to optimize resource allocation and workload management.
Problems Solved: - Efficient utilization of processing units in computing systems. - Improved performance and user experience for applications running on integrated and discrete processing units.
Benefits: - Enhanced application performance and responsiveness. - Better resource utilization and energy efficiency in computing systems.
Commercial Applications: Title: "Dynamic Application Execution Assignment Technology for Enhanced Performance" This technology can be utilized in smartphones, tablets, laptops, and servers to improve overall system performance and user satisfaction. It can also be integrated into cloud computing platforms to optimize resource allocation and enhance scalability.
Questions about the technology: 1. How does this technology improve the overall performance of applications running on computing systems? - By dynamically assigning tasks to the most suitable processing unit based on usage data and performance metrics, this technology ensures optimal resource utilization and enhances application responsiveness.
2. What are the potential implications of using machine learning to assign application execution tasks in computing systems? - By leveraging machine learning algorithms, this technology can adapt to changing workload demands and optimize resource allocation in real-time, leading to improved system performance and user experience.
Original Abstract Submitted
Apparatuses, systems, and techniques for assigning execution of applications to various processing units using machine learning are disclosed herein. Usage data for an application to be executed using a computing system including an integrated processing unit and a discrete processing unit is identified. At least a portion of operations of the application to be executed using the integrated processing unit or the discrete processing unit based on the usage data and in view of at least one of one or more system performance metrics or one or more user experience metrics associated with executing the application using the integrated processing unit and the discrete processing unit.
