Kernel Attribute Storage for Compatible Parallel Kernel Launches
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Solution Overview
Problem
Existing software kernels face suboptimal performance due to increased complexity and compatibility issues with changing driver and software versions, leading to inefficiencies and potential incompatibilities.
Innovation Solution
A data structure is utilized to encode attribute values for software kernels, which are used to configure and launch kernels through an API function, ensuring optimal performance by defining characteristics and configurations, such as type, parameters, and policies, thereby addressing complexity and compatibility challenges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If software kernel complexity increases to handle more operations, then functionality improves, but performance deteriorates
Solution Approach 1:
The patent segments the software kernel into multiple smaller kernels, each optimized for specific operations. The kernel compiler automatically divides a complex kernel into several simpler kernels that can be executed independently and in parallel, resolving the contradiction by maintaining functionality while improving performance through reduced complexity of individual kernels.
Solution Approach 2:
The patent introduces dynamic kernel selection and configuration mechanisms where kernels can be chosen and adjusted at runtime based on input data characteristics and hardware capabilities. This allows the system to adapt kernel complexity dynamically - using simpler kernels when possible and more complex ones only when necessary, thus maintaining performance while preserving functionality.
2Adaptability or versatility
If driver and software versions change to improve capabilities, then functionality improves, but compatibility deteriorates
Solution Approach 1:
The patent implements preliminary compatibility checking and version detection mechanisms that verify driver and software version compatibility before kernel execution. The system pre-validates compatibility requirements and adjusts kernel configuration accordingly, preventing compatibility issues before they occur while still allowing access to new capabilities in updated versions.
Solution Approach 2:
The patent uses parameter changes to maintain compatibility across different software versions. By adjusting kernel parameters, configuration options, and execution settings based on detected driver versions, the system can accommodate capability improvements in newer versions while maintaining functional compatibility with older versions through parameter adaptation.
3Adaptability or versatility
If kernel configuration options increase to handle various scenarios, then adaptability improves, but device complexity increases
Solution Approach 1:
The patent implements self-service mechanisms where the kernel compiler and runtime system automatically select and configure appropriate kernel parameters based on input data characteristics, hardware capabilities, and execution context. This automation reduces the burden on users to manually configure complex parameters while maintaining high adaptability, as the system serves itself by making intelligent configuration decisions.
Solution Approach 2:
The patent creates universal kernel configurations that can handle multiple scenarios through a standardized interface. By designing kernels with universal parameter sets that cover common use cases and allowing automatic specialization through compiler optimizations, the system reduces configuration complexity while maintaining adaptability across different execution scenarios.
Data Source
AI summary
Apparatuses, systems, and techniques to cause one or more software kernels to be performed based, at least in part, on a data structure to indicate one or more attributes of the one or more software kernels. In at least one embodiment, one or more software kernels are caused to be performed based, at least in part, on a data structure to indicate one or more attributes of the one or more software kernels based on, for example, CUDA or other parallel computing platform code.