Deep Learning Operation Placement Optimizing Resource Utilization
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Solution Overview
Problem
Current deep learning cluster schedulers face low resource utilization due to uniform resource placement strategies, which lead to idle resources and inefficient data transmission, as they typically allocate GPUs at a granularity that does not effectively manage massive training data.
Innovation Solution
A method and system for operation resource placement in deep learning that optimizes resource allocation by selecting network structures based on training operation priorities and required resource amounts, minimizing network data transmission through parameter servers and workers placement, and utilizing a multi-layer network structure to improve resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If uniform resource placement strategy is used, then implementation simplicity is improved, but resource utilization deteriorates
Solution Approach 1:
The patent segments the cluster into multiple layers (node layer, rack layer, pod layer) and further divides resources into fine-grained units (GPU cores, CPU cores, memory). This segmentation enables differentiated resource placement strategies for different training operations, improving resource utilization while maintaining manageable complexity through hierarchical organization.
Solution Approach 2:
The patent implements dynamic resource placement by continuously monitoring training operation characteristics, resource availability, and network conditions. The system adjusts placement decisions in real-time based on current cluster state, transforming the static uniform placement into a dynamic adaptive process that optimizes resource utilization.
2Productivity
If fine-grained resource allocation is implemented, then resource utilization is improved, but system complexity increases
Solution Approach 1:
The patent manages fine-grained resources by segmenting them into hierarchical layers (node, rack, pod) and resource types (compute, storage, network). This segmentation approach allows the system to handle detailed resource allocation without overwhelming complexity, as each layer manages specific resource aspects independently.
Solution Approach 2:
The patent changes the granularity parameter of resource allocation from coarse (whole GPU) to fine (GPU cores, CPU cores, memory units). By systematically adjusting this parameter across different resource types and layers, the system achieves high utilization while maintaining consistent management patterns that control complexity.
3Loss of information
If training operations are integrated to servers with sufficient resources, then network communication is reduced, but resource utilization deteriorates due to idle resources
Solution Approach 1:
The patent applies local quality by placing different training operations with different resource requirements and network communication patterns into appropriate locations within the cluster hierarchy. Operations requiring high network bandwidth are placed on servers with sufficient network resources, while compute-intensive operations are placed on servers with strong processing capabilities, optimizing both network communication and resource utilization locally.
Solution Approach 2:
The system implements feedback mechanisms that monitor network communication patterns and resource utilization continuously. Based on this feedback, the placement strategy is adjusted to balance network communication efficiency with resource utilization, preventing the formation of idle resources while minimizing network overhead.
Data Source
AI summary
A method, a system, a device, and a storage medium for operation resource placement of deep learning are provided. The method includes: acquiring training operations to be placed and corresponding priorities; based on an order of the priorities, selecting a network structure for operation placement according to required resource amount of the training operations in sequence; the network structure including a server, a top of rack, a container group set denoted as Podset and a trunk layer switch; based on the selected network structure, taking a transmission amount of network data in a training process as an optimization target to perform minimization optimization, and obtaining a corresponding operation placement scheme.