Parallel Processor Task Launch Latency via Dependency Decoupling
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Solution Overview
Problem
Parallel processors experience significant launch latency due to time-consuming overhead operations such as memory flushes and scheduling dependencies, which can exceed the time required to execute tasks, especially in software applications with short execution times.
Innovation Solution
A parallel processor with a work scheduler/distribution unit that decouples scheduling dependencies from data dependencies, allowing the execution of consumer tasks to overlap with producer tasks by resolving scheduling dependencies during task execution and using pre-exit triggers to manage memory flushes and prefetch operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the parallel processor waits for all producer task threads to finish and performs memory flushes before launching consumer tasks, then data dependencies are resolved correctly, but launch latency increases significantly
Solution Approach 1:
The patent applies preliminary action by resolving scheduling dependencies before the producer task completes execution. The work scheduler/distribution unit resolves scheduling dependencies based on pre-exit triggers, allowing consumer tasks to be launched before the producer task fully completes. This preliminary resolution of scheduling dependencies (separate from data dependencies) enables the consumer task to start earlier, reducing launch latency while maintaining correctness through subsequent data dependency checks.
2Reliability
If the parallel processor performs memory flushes and scheduling operations after producer task completion, then data consistency is maintained, but overall execution time increases
Solution Approach 1:
The patent segments the dependency resolution process into two independent parts: scheduling dependencies and data dependencies. Scheduling dependencies (related to task coordination and resource allocation) are resolved preliminarily based on pre-exit triggers, allowing consumer tasks to launch earlier. Data dependencies (related to actual data availability) continue to be enforced through memory flushes and consistency checks. This segmentation allows overlapping of producer and consumer task execution, improving productivity while maintaining data consistency through the preserved data dependency checks.
3Device complexity
If the parallel processor launches consumer tasks only after producer task completion, then scheduling simplicity is maintained, but resource utilization decreases
Solution Approach 1:
The patent applies preliminary action by launching consumer tasks before the producer task completes, based on pre-exit triggers. The work scheduler/distribution unit resolves scheduling dependencies in advance, allowing the consumer task to be launched and even begin execution while the producer task is still running. This preliminary launching improves resource utilization by keeping processing units busy, while scheduling complexity remains manageable because data dependency checks are still performed to ensure correctness.
Data Source
AI summary
In various embodiments, scheduling dependencies associated with tasks executed on a processor are decoupled from data dependencies associated with the tasks. Before the completion of a first task that is executing in the processor, a scheduling dependency specifying that a second task is dependent on the first task is resolved based on a pre-exit trigger. In response to the resolution of the scheduling dependency, the second task is launched on the processor.