Lookup Data Pipelining for Model Parallelism Latency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing recommendation models face significant latency due to the all to all communication of lookup data between nodes, which delays the processing of input data, especially when using model parallelism where large embedding tables are distributed across multiple nodes.
Innovation Solution
The proposed solution involves pipelining the generation and communication of lookup data, where each node generates and communicates independent portions of the lookup data simultaneously, allowing data consuming nodes to perform operations without waiting for complete data, thereby reducing latency and improving processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If model parallelism is used to distribute large embedding tables across multiple nodes, then memory requirements per node are reduced, but communication latency increases due to all-to-all communication of lookup data
Solution Approach 1:
The patent segments the lookup data into multiple independent portions and divides the communication process into stages. Each node communicates only the necessary portions of lookup data to other nodes rather than transmitting all data, reducing communication volume and latency while maintaining model parallelism benefits.
Solution Approach 2:
The patent performs preliminary actions by pre-computing and pre-distributing lookup data portions before the main processing operation. This allows nodes to have lookup data ready in advance, reducing communication latency during the critical processing phase while maintaining distributed memory architecture.
2Reliability
If all-to-all communication of lookup data is performed between nodes, then data availability for model operations is ensured, but processing speed decreases due to sequential dependency
Solution Approach 1:
The patent segments the data communication process into independent portions that can be transmitted and processed in parallel. Each node communicates specific portions of lookup data independently, allowing other nodes to perform model operations simultaneously, thus maintaining data availability while improving processing speed.
Solution Approach 2:
The patent introduces dynamic communication where nodes can communicate different portions of lookup data at different times based on processing needs. This dynamic approach allows the system to maintain data availability when needed while improving overall processing speed by overlapping communication and computation.
3Stability of the object's composition
If lookup data is generated and communicated completely before model operations, then data consistency is ensured, but latency increases due to sequential processing
Solution Approach 1:
The patent performs preliminary generation and communication of lookup data portions before model operations. By pre-generating and pre-communicating the necessary data portions, the system ensures data consistency is established in advance while reducing overall latency by overlapping data preparation with model processing.
Solution Approach 2:
The patent segments the data consistency requirement into multiple smaller consistency checks for different data portions. This allows the system to verify consistency for each portion independently and in parallel, maintaining data consistency while reducing the time required compared to verifying all data sequentially.
Data Source
AI summary
An electronic device includes one or more data producing nodes and a data consuming node. Each data producing node separately generates two or more portions of a respective block of data. Upon completing generating each portion of the two or more portions of the respective block of data, each data producing node communicates that portion of the respective block of data to the data consuming node. Upon receiving corresponding portions of the respective blocks of data from each of the one or more data producing nodes, the data consuming node performs operations for a model using the corresponding portions of the respective blocks of data.