GPU Work Queue Management via Local Command Blocks
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Solution Overview
Problem
Current GPU architectures are limited by CPU-to-GPU communication channels, such as PCI-E, which prevent the GPU from modifying the GP_PUT pointer, necessitating CPU intervention for nested work generation, leading to inefficiencies.
Innovation Solution
A method is introduced where threads on the GPU locally generate work by creating command blocks, using semaphores to manage the GP_GET and GP_PUT pointers, allowing the GPU to autonomously manage its work queue and execute nested tasks without CPU authorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the GPU relies on the CPU to generate and execute nested work, then the system can maintain proper work queue management through the GP_PUT pointer, but the processing efficiency decreases and CPU workload increases
Solution Approach 1:
The GPU is enabled to autonomously generate nested work and update the GP_PUT pointer without CPU intervention. The GPU independently manages its own work queue by allocating command blocks and updating pointers, thereby serving itself rather than relying on the CPU for these operations.
Solution Approach 2:
The work queue management is segmented into distinct components: the GP_GET pointer managed by the GPU for tracking completed work, and the GP_PUT pointer updated by the GPU for submitting new work. This segmentation allows the GPU to independently control both pointers without CPU involvement, resolving the contradiction between efficiency and management complexity.
2Productivity
If the GPU autonomously manages the work queue and updates the GP_PUT pointer, then processing efficiency improves and CPU workload reduces, but hardware limitations of CPU-to-GPU communication channels prevent this capability
Solution Approach 1:
A command block structure is introduced as an intermediary data structure that the GPU uses to submit nested work to itself. The command blocks contain instructions and parameters that enable the GPU to autonomously manage the work queue without requiring direct CPU-GPU communication for pointer updates, thereby working around hardware limitations.
Solution Approach 2:
The system transitions from a single-dimensional CPU-controlled work submission model to a multi-dimensional model where the GPU operates in its own dimension, independently generating and submitting nested work through command blocks while the CPU handles only top-level work submission.
3Reliability
If the CPU generates all GPU work entries, then proper work queue management is ensured, but bandwidth consumption across CPU-GPU communication paths increases
Solution Approach 1:
The GPU performs self-service by generating nested work entries and updating the GP_PUT pointer autonomously. This eliminates the need for continuous CPU-GPU communication for work queue management, significantly reducing bandwidth consumption while maintaining reliability through the GPU's independent control of the work queue.
Solution Approach 2:
The GPU prepares and submits nested work entries in advance without waiting for CPU authorization. By performing the work generation and queue update actions preliminarily and autonomously, the system reduces communication overhead while ensuring work queue integrity through the GPU's direct control.
Data Source
AI summary
One embodiment of the present invention enables threads executing on a processor to locally generate and execute work within that processor by way of work queues and command blocks. A device driver, as an initialization procedure for establishing memory objects that enable the threads to locally generate and execute work, generates a work queue, and sets a GP_GET pointer of the work queue to the first entry in the work queue. The device driver also, during the initialization procedure, sets a GP_PUT pointer of the work queue to the last free entry included in the work queue, thereby establishing a range of entries in the work queue into which new work generated by the threads can be loaded and subsequently executed by the processor. The threads then populate command blocks with generated work and point entries in the work queue to the command blocks to effect processor execution of the work stored in the command blocks.