Command Stream Wait Commands for Parallel Task Execution
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Solution Overview
Problem
Data processing systems, particularly those utilizing graphics processors, face inefficiencies in task execution due to serial dependencies and uneven resource utilization, leading to suboptimal performance and increased host processor loading when modifying command streams to insert smaller tasks for parallel execution.
Innovation Solution
The method involves preparing multiple command streams with 'wait' commands that cause the processing resource to delay executing subsequent commands until it reaches a specific position in another command stream, facilitating more efficient synchronization and resource utilization without significant host processor overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If smaller tasks are inserted into the command stream to improve resource utilization, then processing resource utilization is improved, but driver complexity and host processor loading increase significantly
Solution Approach 1:
The command stream is segmented into multiple independent command streams, each handling specific tasks. This allows the processing resource to execute tasks in parallel across different command streams without requiring complex driver operations to insert and track individual tasks within a single stream.
Solution Approach 2:
Multiple command streams are prepared in advance with tasks organized and ordered before submission to the processing resource. This preliminary organization eliminates the need for real-time driver intervention to insert tasks and track modification points during execution.
2Ease of operation
If tasks are executed in serial order to maintain simplicity, then driver operation simplicity is maintained, but processing resource utilization deteriorates
Solution Approach 1:
The single serial command stream is divided into multiple command streams that can be executed in parallel. Each command stream maintains simple ordering rules, but the system as a whole achieves parallel execution and improved resource utilization.
Solution Approach 2:
The system transitions from a single-dimensional serial execution model to a multi-dimensional parallel execution model by introducing multiple command streams. This allows tasks to be distributed across different execution dimensions while maintaining simplicity within each dimension.
3Productivity
If tasks are inserted at earlier points in the command stream to enable parallel execution, then processing efficiency is improved, but host processor loading increases due to tracking requirements
Solution Approach 1:
Task insertion points and execution timing are determined in advance during command stream preparation. The host processor does not need to continuously track and dynamically insert tasks during execution, reducing real-time processing load while maintaining efficient parallel execution.
Solution Approach 2:
Tasks are organized into separate command streams with predetermined execution orderings. This segmentation eliminates the need for the host processor to track complex insertion points within a single stream, reducing hosting processor loading while enabling parallel execution.
Data Source
AI summary
When a processing resource of a data processing system is to perform processing tasks for applications executing on a host processor, the host processor prepares a plurality of command streams to cause the processing resource to perform the processing tasks. When a processing task to be added to a command stream has a dependency on a processing task or tasks that will be included in another command stream, a wait command is added to the command stream that is to include the processing task that has a dependency on a processing task or tasks that will be included in the another command stream, to cause the processing resource to delay executing subsequent commands in the command stream after the wait command, until the processing resource has reached a particular position in the another command stream.