Preemptive Task Mechanism for Distributed ML Straggler Mitigation
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Solution Overview
Problem
Current approaches to mitigating stragglers in distributed machine learning training, such as relaxed consistency models and worker replication, fail to effectively address static performance differences in heterogeneous systems, leading to inefficiencies and increased resource usage.
Innovation Solution
Implementing a preemptive task mechanism where tasks are assigned multiple training data samples and preempted if they haven't finished processing, allowing for dynamic time allocation and updating the cognitive model with outputs from both preempted and completed tasks, thereby exploiting the distributed environment to accelerate training without additional resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If worker replication is used to mitigate stragglers, then reliability of training completion is improved, but resource usage increases
Solution Approach 1:
The system pre-assigns multiple training data samples to each task before execution. This preliminary assignment enables the task to be preempted at optimized checkpoints, allowing the training to proceed without waiting for straggling tasks to complete all their assigned samples, thus improving reliability without requiring additional worker replicas
Solution Approach 2:
The invention changes the parameter of task execution by introducing preemptible tasks with early exit points. Instead of running tasks to completion or using backup workers, the system modifies the execution parameter to allow interruption at predetermined checkpoints, enabling efficient resource utilization while maintaining training reliability
2Productivity
If relaxed consistency models are used to address stragglers, then productivity is improved, but manufacturing precision of model updates deteriorates
Solution Approach 1:
The system pre-defines checkpoints within each task where the model can be safely updated. These preliminary action points are strategically placed to ensure that even if a task is preempted, the model receives updates from completed portions, maintaining precision while improving productivity through parallel execution
Solution Approach 2:
The training task is segmented into multiple processing stages with defined checkpoints. Each segment can be independently processed and preempted, allowing the system to aggregate partial results without compromising overall model update accuracy while enabling faster parallel training execution
3Manufacturing precision
If tasks are run to completion in distributed environment, then manufacturing precision of model updates is maintained, but loss of time increases due to stragglers
Solution Approach 1:
The system implements the ability to skip the completion of full task execution by preempting straggling tasks at optimized checkpoints. This rushing through mechanism allows the training to proceed without waiting for slow tasks, reducing training time while maintaining model update precision through careful checkpoint selection
Solution Approach 2:
The system monitors task execution progress and provides feedback to determine when to preempt straggling tasks. This feedback mechanism enables the system to identify when a task is falling behind and trigger preemption at the optimal checkpoint, balancing time efficiency with model update accuracy
Data Source
AI summary
Embodiments of the present invention provide computer-implemented methods, computer program products and systems. Embodiments of the present invention can run preemptable tasks distributed according to a distributed environment, wherein each task of a plurality of preemptable tasks has been assigned two or more of the training data samples to process during each iteration. Embodiments of the present invention can, upon verifying that a preemption condition for each iteration is satisfied: preempt any task of the preemptable tasks that have started processing training data samples assigned to it, and update the cognitive model based on outputs obtained from completed tasks, including outputs obtained from both the preempted tasks and completed tasks that have finished processing all training data samples as assigned to it.