Dynamic Network POD Allocation for LLM Task Parallelism
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Solution Overview
Problem
Traditional network architectures in distributed computing are often static and inefficient for handling the computational demands of modern applications like deep learning, leading to issues such as network congestion, latency, overprovisioning, and underprovisioning, which affect the performance and resource utilization of large language models (LLMs).
Innovation Solution
A dynamic, structured hierarchical network is introduced, utilizing hosts, leaf switches, and optical switches to configure a network point of delivery (POD) based on parallelism parameters and communication patterns, enabling efficient allocation of resources through pipeline and data parallelism, and employing a scalable hierarchical aggregation and reduction protocol (SHARP) for optimal data exchange.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional static network architectures are used, then network structure is simple and stable, but resource utilization is inefficient and cannot adapt to computational demands
Solution Approach 1:
The patent implements dynamic network topology that automatically reconfigures based on computational workload. The system transitions from static to dynamic architecture by introducing controllers that monitor computational demands and adjust network connections in real-time, enabling the network to adapt its structure to match the varying requirements of machine learning workloads.
Solution Approach 2:
The network is divided into modular units including hosts, switches, and controllers that can be independently configured and managed. This segmentation allows flexible reorganization of network resources into different topologies (fat-tree, dragonfly, mesh) based on specific computational needs, resolving the contradiction between adaptability and complexity.
2Reliability
If network resources are overprovisioned, then performance requirements are met, but resource waste increases
Solution Approach 1:
The system dynamically adjusts network parameters such as bandwidth allocation, topology configuration, and routing paths based on actual workload conditions. By changing these parameters in real-time, the network maintains performance guarantees during high-demand periods while reducing resource consumption during low-utilization periods, eliminating the need for permanent overprovisioning.
Solution Approach 2:
The patent implements feedback mechanisms where controllers monitor network performance metrics and computational workload, then adjust resource allocation accordingly. This closed-loop control ensures that network resources are provisioned appropriately to meet performance requirements without excessive waste, as resources are scaled up or down based on actual needs.
3Loss of energy
If network resources are underprovisioned, then resource efficiency is improved, but network congestion and latency increase
Solution Approach 1:
The dynamic reconfiguration capability allows the network to optimize its topology and resource allocation based on real-time workload conditions. During high-demand periods, the network automatically activates additional paths and increases bandwidth allocation to prevent congestion, while during low-demand periods, it reduces active resources to maintain efficiency, thus avoiding both underprovisioning and permanent overprovisioning.
4Ease of manufacture
If static network topologies are used, then implementation is simple, but they are suboptimal for handling modern computational demands
Solution Approach 1:
The patent designs a universal network platform that can implement multiple topology types (fat-tree, dragonfly, mesh, etc.) through software configuration rather than requiring different hardware architectures for each topology. This multi-functionality allows the system to maintain implementation simplicity while achieving optimal data exchange efficiency for various computational workloads by selecting and configuring the appropriate topology dynamically.
Data Source
AI summary
Systems, computer program products, and methods are described herein for allocation of network resources for executing large language model (LLM) tasks. An example system receives an LLM task and an input specifying information associated with execution of the LLM task, wherein the input comprises at least a parallelism parameter and a communication pattern; determines a plurality of hosts based on at least the parallelism parameter and the communication pattern; determines a plurality of switches based on the plurality of hosts; operatively couples the plurality of hosts to the plurality of switches to configure a network point of delivery (POD); and triggers execution of the LLM task using the network POD.