Advanced micro devices, inc. (20240111591). Executing Kernel Workgroups Across Multiple Compute Unit Types simplified abstract
Contents
- 1 Executing Kernel Workgroups Across Multiple Compute Unit Types
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Executing Kernel Workgroups Across Multiple Compute Unit Types - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
Executing Kernel Workgroups Across Multiple Compute Unit Types
Organization Name
Inventor(s)
Bradford Michael Beckmann of Kirkland WA (US)
Sooraj Puthoor of Austin TX (US)
Executing Kernel Workgroups Across Multiple Compute Unit Types - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240111591 titled 'Executing Kernel Workgroups Across Multiple Compute Unit Types
Simplified Explanation
The abstract of the patent application describes a method for optimizing the execution of kernels by monitoring the usage of compute units and dynamically assigning workgroups to different types of compute units based on their availability.
- The innovation involves dividing kernels into workgroups and dynamically assigning them to different types of compute units based on their usage, such as CPU cores or GPU cores.
- The system monitors the usage of one type of compute unit and switches workgroups targeting another type of compute unit when the first type is idle, optimizing resource utilization.
- For example, if CPU cores are idle, workgroups targeting GPU cores are executed on the CPU cores, improving overall performance and efficiency.
Potential Applications
This technology could be applied in various fields such as:
- High-performance computing
- Artificial intelligence
- Data processing and analytics
Problems Solved
This technology addresses the following issues:
- Efficient resource utilization in heterogeneous computing environments
- Optimizing performance of parallel processing tasks
Benefits
The benefits of this technology include:
- Improved performance and efficiency in executing kernels
- Dynamic resource allocation based on workload demands
- Enhanced scalability in parallel computing tasks
Potential Commercial Applications
Potential commercial applications of this technology include:
- Cloud computing services
- Scientific research institutions
- Gaming industry for graphics processing
Possible Prior Art
One possible prior art for this technology could be dynamic task scheduling algorithms in parallel computing systems.
Unanswered Questions
How does this technology impact energy consumption in compute units?
This article does not address the potential impact of this technology on energy consumption in compute units. Further research is needed to understand the energy efficiency implications of dynamic workgroup assignment.
What are the security implications of dynamically assigning workgroups to different compute units?
The article does not discuss the security aspects of dynamically assigning workgroups to different compute units. Future studies should investigate the potential security risks and mitigation strategies associated with this technology.
Original Abstract Submitted
portions of programs, oftentimes referred to as kernels, are written by programmers to target a particular type of compute unit, such as a central processing unit (cpu) core or a graphics processing unit (gpu) core. when executing a kernel, the kernel is separated into multiple parts referred to as workgroups, and each workgroup is provided to a compute unit for execution. usage of one type of compute unit is monitored and, in response to the one type of compute unit being idle, one or more workgroups targeting another type of compute unit are executed on the one type of compute unit. for example, usage of cpu cores is monitored, and in response to the cpu cores being idle, one or more workgroups targeting gpu cores are executed on the cpu cores.
