arXiv:2505.23131v2 Announce Type: replace Abstract: We study the problem of assigning operations in a dataflow graph to devices to minimize execution time in a work-conserving system, with emphasis on complex machine learning workloads. Prior learning-based methods often struggle due to three key limitations: (1) reliance on bulk-synchronous systems like TensorFlow, which under-utilize devices due to barrier synchronization; (2) lack of awareness of the scheduling mechanism of underlying systems when designing learning-based methods; and (3) exclusive dependence on reinforcement learning, igno
The increasing complexity of machine learning workloads and the need to optimize computational efficiency in asynchronous dataflow graphs are driving innovation in device assignment.
Efficient device assignment is critical for maximizing the utilization of computing resources, especially for complex AI models, directly impacting the speed and cost of AI development and deployment.
This research introduces a dual-policy learning approach that addresses key limitations of prior methods, potentially leading to more efficient and adaptable resource management in AI infrastructure.
- · AI compute providers
- · Hyperscalers
- · Machine learning researchers
- · Hardware manufacturers
- · Inefficient AI frameworks
- · Traditional workload schedulers
Improved performance and reduced latency for complex AI models in production environments.
Lower operational costs for AI infrastructure, enabling more widespread and ambitious AI applications.
Accelerated development cycles for new AI paradigms due to more accessible and powerful compute.
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Read at arXiv cs.LG