Distributed Inference Engine Parallelism Switching for Lower Latency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing AI computer systems face latency and runtime issues due to the transition from data parallelism to bandwidth-constrained processing in distributed inference engines, leading to inefficiencies.
Innovation Solution
A distributed inference engine system that dynamically switches between data parallelism and tensor parallelism, using a leader inference engine to divide data into shards and generate tokens in parallel, with encryption and synchronization to manage communication and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data parallelism is used in distributed inference engines, then processing speed is improved, but latency increases due to transition to bandwidth-constrained processing
Solution Approach 1:
The system dynamically switches between data parallelism and tensor parallelism modes based on processing stage requirements. During initial processing stages, data parallelism is used for speed, while tensor parallelism is activated in later stages to reduce latency and bandwidth constraints, optimizing the trade-off between processing speed and latency throughout the inference pipeline.
2Productivity
If distributed inference engines process large data sets, then productivity is improved, but power consumption increases
Solution Approach 1:
The inference process is segmented into multiple stages with different parallelism strategies. Large data sets are divided and processed in batches, with each stage using optimized parallelism (data or tensor) appropriate to the data size and processing requirements, reducing overall power consumption while maintaining high productivity through staged processing.
Solution Approach 2:
The system changes parallelism parameters dynamically based on data size and processing stage. When processing large data sets, the system transitions from data parallelism to tensor parallelism, adjusting computational parameters to optimize the balance between processing capacity and power consumption for different data volumes.
3Productivity
If multiple inference engines are used in parallel, then processing capacity is improved, but system complexity increases
Solution Approach 1:
Multiple inference engines are designed with universal functionality to operate in different parallelism modes (data parallelism, tensor parallelism) and handle various processing stages. This multi-functionality allows the same hardware infrastructure to efficiently handle different workloads and parallelism configurations, improving processing capacity without proportionally increasing system complexity.
Solution Approach 2:
The distributed inference engine system implements feedback mechanisms to monitor processing performance, data size, and system state. Based on this feedback, the system automatically selects appropriate parallelism strategies and distributes workloads optimally across multiple inference engines, managing system complexity through intelligent control while maximizing processing capacity.
Data Source
AI summary
A distributed inference engine system that includes multiple inference engines is disclosed. A particular inference engine of the multiple inference engines may receive a prompt and its associated data, and divide the data into multiple data portions that are distributed to the multiple inference engines. Operating in parallel, and using a machine-learning model and respective data portions, the multiple inference engines generate an initial token. The multiple inference engines also generate, in parallel and using corresponding portions of the machine-learning model and the initial token, a subsequent token.