Apple inc. (20240345892). Compute Kernel Parsing with Limits in one or more Dimensions simplified abstract
Contents
- 1 Compute Kernel Parsing with Limits in one or more Dimensions
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Compute Kernel Parsing with Limits in one or more Dimensions - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Optimal Compute Work Distribution Technology
- 1.13 Original Abstract Submitted
Compute Kernel Parsing with Limits in one or more Dimensions
Organization Name
Inventor(s)
Andrew M. Havlir of Orlando FL (US)
Ajay Simha Modugala of Orlando FL (US)
Karl D. Mann of Geneva FL (US)
Compute Kernel Parsing with Limits in one or more Dimensions - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240345892 titled 'Compute Kernel Parsing with Limits in one or more Dimensions
Simplified Explanation
The patent application describes techniques for dispatching compute work from a compute stream, particularly on a graphics processor executing compute kernels. The workload parser circuitry determines sets of workgroups from compute kernels organized in multiple dimensions, optimizing the distribution of work to the graphics processor.
- The workload parser circuitry determines sets of workgroups from compute kernels organized in multiple dimensions.
- The technique involves determining multiple sub-kernels for the compute kernel to optimize workload distribution.
- The parser circuitry iterates through workgroups in different dimensions to generate the set of workgroups efficiently.
- The disclosed techniques aim to provide desirable shapes for batches of workgroups.
Key Features and Innovation
- Optimization of compute work distribution from compute kernels.
- Efficient determination of workgroups organized in multiple dimensions.
- Iterative process for generating sets of workgroups for graphics processor execution.
- Focus on providing optimal shapes for batches of workgroups.
Potential Applications
The technology can be applied in various fields such as:
- High-performance computing
- Graphics processing
- Data processing and analysis
- Artificial intelligence and machine learning
Problems Solved
- Efficient dispatching of compute work from compute streams.
- Optimization of workload distribution for graphics processors.
- Improved organization of workgroups in multiple dimensions.
Benefits
- Enhanced performance in executing compute kernels.
- Increased efficiency in workload distribution.
- Optimal shapes for batches of workgroups.
- Improved utilization of graphics processor resources.
Commercial Applications
Title: Optimal Compute Work Distribution Technology This technology can have commercial applications in:
- Gaming industry for graphics processing
- Data centers for high-performance computing
- AI and machine learning applications
- Scientific research for data analysis
Prior Art
Readers interested in prior art related to this technology can explore research papers, patents, and publications in the fields of graphics processing, high-performance computing, and workload optimization.
Frequently Updated Research
Researchers are constantly exploring new techniques for optimizing workload distribution in compute kernels, particularly in the context of graphics processing and high-performance computing.
Questions about Optimal Compute Work Distribution Technology
What are the key benefits of optimizing workload distribution in compute kernels?
Optimizing workload distribution leads to improved performance, efficiency, and resource utilization in executing compute kernels.
How does the iterative process of generating sets of workgroups enhance the efficiency of graphics processor execution?
By iterating through workgroups in different dimensions, the technology ensures that work is distributed optimally, leading to better performance and resource management.
Original Abstract Submitted
techniques are disclosed relating to dispatching compute work from a compute stream. in some embodiments, a graphics processor executes instructions of compute kernels. workload parser circuitry may determine, for distribution to the graphics processor circuitry, a set of workgroups from a compute kernel that includes workgroups organized in multiple dimensions, including a first number of workgroups in a first dimension and a second number of workgroups in a second dimension. this may include determining multiple sub-kernels for the compute kernel, wherein a first sub-kernel includes, in the first dimension, a limited number of workgroups that is smaller than the first number of workgroups. the parser circuitry may iterate through workgroups in both the first and second dimensions to generate the set of workgroups, proceeding through the first sub-kernel before iterating through any of the other sub-kernels. disclosed techniques may provide desirable shapes for batches of workgroups.
