DLRM Leaf-Switch Network Allocation for Dynamic Data Exchange
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Solution Overview
Problem
Traditional network architectures in distributed computing, particularly for deep learning tasks, suffer from inefficiencies such as network congestion, latency, and lack of adaptability, leading to issues like overprovisioning and underprovisioning, which hinder performance and resource utilization.
Innovation Solution
A dynamic, structured hierarchical network is introduced, utilizing leaf and optical switches to form a complete graph structure, dynamically allocating resources based on communication patterns and bandwidth requirements, ensuring efficient data flow and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional static network architectures are used, then network structure is simple and easy to implement, but resource utilization is inefficient and adaptability is poor
Solution Approach 1:
The patent implements dynamic network topology configuration where the system automatically adjusts network connections based on task requirements. The controller determines communication patterns and configures network switches dynamically, transforming the static network architecture into a dynamic one that adapts to different computational workloads and data exchange needs.
Solution Approach 2:
The patent segments the network into hierarchical layers (core layer, aggregation layer, access layer) with different functional responsibilities. This segmentation allows each layer to be optimized independently while maintaining overall system adaptability, resolving the contradiction by organizing complexity into manageable segments.
2Productivity
If network resources are overprovisioned to ensure sufficient bandwidth, then data exchange performance is improved, but resource waste increases
Solution Approach 1:
The patent changes network parameters (bandwidth allocation, connection topology) dynamically based on actual task requirements. The controller analyzes communication patterns and adjusts network configuration parameters to match the specific needs of each computational task, avoiding both overprovisioning and underprovisioning.
Solution Approach 2:
The system implements self-service resource allocation where the controller automatically determines and configures optimal network resources based on task characteristics without manual intervention. This eliminates the need for conservative overprovisioning while ensuring sufficient performance through automated, precise resource matching.
3Power
If more optical circuit connections are added to increase bandwidth, then data exchange capacity is improved, but network complexity and cost increase
Solution Approach 1:
The patent makes network switches multi-functional by enabling them to operate in different modes (routing, switching, optical circuit switching) and to be configured for various topologies (fat-tree, mesh, ring) based on task requirements. This universality allows the same hardware infrastructure to provide different bandwidth levels and configurations without adding dedicated hardware for each scenario.
4Loss of time
If static network topology is used, then implementation is simple, but training time for computationally intensive tasks increases
Solution Approach 1:
The patent performs preliminary action by pre-configuring optimal network topologies and communication patterns based on task characteristics before execution. The controller analyzes the computational task and pre-establishes the appropriate network configuration, so that when the task runs, data exchange is already optimized, reducing training time without requiring complex real-time adjustments.
Data Source
AI summary
Systems, computer program products, and methods are described herein for allocation of network resources for executing deep learning recommendation model (DLRM) tasks. An example system receives a task and an input specifying information associated with execution of the task, wherein the input comprises a plurality of hosts, determines a plurality of leaf switches based on the plurality of hosts, operatively couples each leaf switch to a subset of the plurality of hosts to configure a network structure; and triggers the execution of the task using the network structure.