17957907. Executing Kernel Workgroups Across Multiple Compute Unit Types simplified abstract (ADVANCED MICRO DEVICES, INC.)
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 Unanswered Questions
- 1.11 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 17957907 titled 'Executing Kernel Workgroups Across Multiple Compute Unit Types
Simplified Explanation
The abstract of the patent application describes a method for dynamically assigning workgroups to different types of compute units based on their availability. This allows for efficient utilization of resources in a computing system.
- The innovation involves monitoring the usage of a particular type of compute unit, such as CPU cores, and dynamically assigning workgroups targeting another type of compute unit, such as GPU cores, when the first type of compute unit is idle.
- By optimizing the allocation of workgroups to different compute units, the system can maximize the overall performance and efficiency of the computing system.
- This technology can be applied in various computing systems, such as high-performance computing clusters, data centers, and cloud computing environments.
- The method solves the problem of underutilization of compute units in a computing system, leading to improved performance and resource efficiency.
- The benefits of this technology include increased overall system performance, reduced energy consumption, and improved scalability of computing systems.
- Potential commercial applications of this technology include cloud computing services, scientific computing applications, and machine learning algorithms.
Potential Applications
This technology can be applied in high-performance computing clusters, data centers, and cloud computing environments.
Problems Solved
This technology solves the problem of underutilization of compute units in a computing system, leading to improved performance and resource efficiency.
Benefits
The benefits of this technology include increased overall system performance, reduced energy consumption, and improved scalability of computing systems.
Potential Commercial Applications
Potential commercial applications of this technology include cloud computing services, scientific computing applications, and machine learning algorithms.
Possible Prior Art
There may be prior art related to dynamic workload allocation in computing systems, but specific examples are not provided in this article.
Unanswered Questions
How does this technology impact the overall cost of operating a computing system?
The article does not address the potential cost savings or cost implications of implementing this technology in a computing system.
What are the potential challenges or limitations of dynamically assigning workgroups to different compute units?
The article does not discuss any potential challenges or limitations that may arise from implementing this technology in practice.
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.
