Computing Interconnect Devices for Distributed Deep Learning Communication
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Solution Overview
Problem
As the number of nodes increases in distributed deep learning systems, the overhead from communication processing and data transmission-reception increases, leading to reduced learning efficiency, particularly when the number of GPUs exceeds 512, causing a significant increase in communication time.
Innovation Solution
A distributed deep learning system with a ring communication network where computing interconnect devices facilitate parallel processing by receiving, sorting, and transmitting data packets between learning nodes, allowing simultaneous calculation and transmission of gradient sums, thereby reducing communication overhead and enhancing cooperative processing among nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of learning nodes is increased to speed up parallel processing, then learning speed is improved, but communication overhead and transmission-reception time increase significantly
Solution Approach 1:
The system segments communication functions by introducing dedicated computing interconnect devices that handle communication tasks separately from learning nodes. This segmentation allows learning nodes to focus on computation while interconnect devices manage data transmission and gradient aggregation, reducing communication overhead for each individual node.
Solution Approach 2:
Computing interconnect devices act as intermediaries between learning nodes, performing gradient sum calculations and data routing. These intermediary devices reduce the direct communication burden on learning nodes by centralizing coordination functions, thereby decreasing overall communication time in the distributed system.
2Power
If the number of GPUs exceeds 512 to enhance parallel processing capability, then computational power is improved, but communication overhead becomes the bottleneck
Solution Approach 1:
Computing interconnect devices perform multiple functions including data reception from learning nodes, gradient sum calculation, result transmission back to nodes, and coordination of communication flows. This multi-functionality reduces the need for specialized communication hardware at each learning node, simplifying the overall system architecture while supporting large numbers of GPUs.
Solution Approach 2:
Each computing interconnect device autonomously performs gradient sum calculations and manages its own communication buffer, eliminating the need for centralized control from learning nodes. This self-service capability reduces communication coordination overhead and allows the system to scale to hundreds of GPUs without proportionally increasing communication complexity.
3Ease of operation
If data transmission and reception are performed sequentially at each learning node, then processing simplicity is maintained, but overall learning efficiency decreases
Solution Approach 1:
The system transitions from a single-dimension sequential processing model to a multi-dimensional parallel architecture where computing interconnect devices operate independently from learning nodes. This dimensional separation enables simultaneous computation at learning nodes and gradient aggregation at interconnect devices, dramatically improving learning efficiency without complicating individual node operations.
Data Source
AI summary
A distributed deep learning system that can achieve speeding-up by processing learning in parallel at a large number of learning nodes connected with a communication network and perform faster cooperative processing among the learning nodes connected through the communication network is provided. The distributed deep learning system includes: a plurality of computing interconnect devices 1 connected with each other through a ring communication network 3 through which communication is possible in one direction; and a plurality of learning nodes 2 connected with the respective computing interconnect devices 1 in a one-to-one relation, and each computing interconnect device 1 executes communication packet transmission-reception processing between the learning nodes 2 and All-reduce processing simultaneously in parallel.