GPU Thread Cancellation API for Duplicate Work Avoidance
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Solution Overview
Problem
Existing GPU thread scheduling technologies fail to address the inefficiencies of GPU thread scheduling, leading to wasted resources and duplicate processing.
Innovation Solution
Implementing an application programming interface (API) to allow running threads to request cancellation of pending threads, confirm cancellation completion, and acquire starting dimensions for new threads, thereby optimizing GPU thread scheduling to minimize resource wastage and prevent duplicate processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If GPU thread scheduling allows separate threads to process work independently, then processing throughput increases, but resource wastage and duplicate processing occur
Solution Approach 1:
The system performs preliminary actions by having running threads request cancellation of pending threads before the pending threads actually execute. This allows the scheduler to proactively prevent duplicate processing by checking if a thread is about to be launched and cancelling it if the work is already being processed, thereby avoiding resource wastage while maintaining throughput
Solution Approach 2:
The invention implements feedback mechanisms where running threads can request cancellation of pending threads based on real-time workload status. The scheduler receives feedback about actual thread execution state and adjusts scheduling decisions accordingly, preventing duplicate processing while maintaining efficient resource utilization
2Device complexity
If GPU threads are scheduled with simple queueing, then implementation complexity is low, but latency increases during task transitions
Solution Approach 1:
The system performs preliminary actions by allowing running threads to request cancellation of pending threads in advance. This enables smoother task transitions by proactively managing thread lifecycle before actual execution begins, reducing transition latency without significantly increasing implementation complexity
Solution Approach 2:
The scheduling system introduces dynamic elements where thread scheduling decisions are made based on real-time requests from running threads. The system dynamically adjusts thread cancellation and allocation based on actual workload conditions, reducing transition latency while maintaining manageable complexity through API-based control
3Loss of energy
If GPU threads can request cancellation of pending threads, then resource utilization improves, but scheduling control complexity increases
Solution Approach 1:
The invention introduces an intermediary layer in the form of an application programming interface (API) that mediates between running threads and the scheduler. This API provides standardized functions for requesting thread cancellation and checking dimensions, improving resource utilization efficiency while containing scheduling control complexity within a well-defined interface layer
4Reliability
If GPU threads check cancellation status before processing, then duplicate processing is prevented, but processing speed decreases
Solution Approach 1:
The system performs preliminary cancellation requests and status checks before threads begin processing work. By establishing thread cancellation status in advance through the API interface, the system prevents duplicate processing while minimizing the time required for status verification, as the check occurs before the main processing loop begins
Data Source
AI summary
Apparatuses, systems, and techniques to cancel pending GPU thread work to allow said work to be assumed by running thread clusters. In at least one embodiment, processors comprising one or more circuits to perform an application programming interface (API) to indicate one or more software threads that have been prevented from being performed by one or more processors.