CUDA Kernel Dependency Management for Execution Scheduling
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Solution Overview
Problem
Existing technologies face challenges in efficiently managing the execution of CUDA programs due to limitations in processing resources, which can lead to delays and inefficiencies.
Innovation Solution
The system employs a processor and a graphics processor to manage kernel dependencies, allowing for the instantiation of kernels on the graphics processor based on indicated dependencies, thereby optimizing execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If kernels are executed independently without dependency management, then execution simplicity is maintained, but execution delays occur due to lack of coordination
Solution Approach 1:
The system performs preliminary actions by having kernels indicate their dependencies in advance before execution. The processor receives and stores dependency information from kernels, preparing the execution schedule beforehand. This allows the system to coordinate kernel launches proactively, preventing execution delays while avoiding complex runtime management.
2Loss of time
If kernel dependencies are managed with detailed coordination, then execution delays are reduced, but processing complexity increases
Solution Approach 1:
The system segments the dependency management process into distinct components: kernels indicate dependencies, the processor receives and stores this information, and the processor determines execution schedules based on stored dependencies. This segmentation allows each component to perform its function simply, reducing overall complexity while still achieving coordinated execution that minimizes delays.
3Productivity
If processors account for all program structuring aspects, then execution efficiency improves, but processor capability requirements increase
Solution Approach 1:
The system introduces an intermediary approach where kernels themselves provide dependency information to the processor, rather than requiring the processor to independently analyze all program structures. This intermediary mechanism allows the processor to efficiently manage execution without needing to understand or account for all aspects of program structuring, maintaining both efficiency and processor versatility.
Data Source
AI summary
Apparatuses, systems, and techniques to execute CUDA programs. In at least one embodiment, one or more software kernels are caused to indicate one or more dependencies among two or more software kernels. In at least one embodiment, one or more software kernels are performed based on one or more kernel dependencies.