Distributed Gradient Reduction Algorithms for Mesh Topologies
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Solution Overview
Problem
Existing machine learning systems face challenges in efficiently processing large datasets in parallel without a central parameter computation subsystem, leading to high latency and inefficiencies in gradient vector combination and parameter update operations.
Innovation Solution
The implementation of various algorithms such as single-path, pincer, rotated pincer, improved rotated pincer, iterative reduction, rotated iterative reduction, recursive reduction, and rotated recursive reduction algorithms for gradient reduction and broadcast operations across multiple processing units organized in circular, torus, and mesh topologies, enabling distributed and efficient processing of training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a central parameter computation subsystem is used, then gradient combination and parameter update operations are simplified, but system complexity and communication overhead increase
Solution Approach 1:
The patent extracts and eliminates the central parameter computation subsystem from the system architecture. Instead of having a central collector gather gradients from all processing units, the system uses distributed reduction operations where each processing unit participates in combining gradients locally, thereby simplifying the overall system structure while maintaining operational effectiveness.
Solution Approach 2:
The processing units perform gradient combination operations autonomously through distributed reduction algorithms. Each processing unit contributes to the reduction process without requiring coordination from a central authority, enabling self-organized gradient aggregation that eliminates the need for complex central management infrastructure.
2Device complexity
If distributed processing without central subsystem is implemented, then system complexity is reduced, but communication latency and processing inefficiency increase
Solution Approach 1:
The patent segments the gradient data into disjoint sets distributed across different processing units. Each processing unit handles a specific subset of gradient elements, and the reduction operation is divided into multiple stages where data is progressively aggregated. This segmentation enables parallel processing while controlling communication latency through structured data distribution.
Solution Approach 2:
The patent introduces a hierarchical dimension to the distributed reduction process. Instead of a flat single-stage reduction, the system organizes processing units in hierarchical levels with reduction operations proceeding through multiple stages. This dimensional organization enables more efficient communication patterns and reduces overall latency by processing gradients in parallel across different hierarchical levels.
3Ease of manufacture
If standard reduction algorithms are used, then implementation is simple, but processing speed and efficiency are insufficient for large datasets
Solution Approach 1:
The patent implements dynamic adaptation in the reduction algorithm based on the specific topology of the processing units. The reduction strategy changes depending on whether units are arranged in linear, tree, mesh, or other configurations, optimizing communication patterns for each topology. This dynamic approach maintains implementation simplicity while significantly improving processing speed for large datasets.
Solution Approach 2:
The patent performs preliminary organization of processing units into topological structures before executing reduction operations. By pre-establishing the hierarchical or network topology, the system can optimize communication paths and reduce the number of communication steps required during gradient aggregation, thereby improving training speed without complicating the reduction algorithm itself.
Data Source
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AI summary
Methods, systems, and apparatus, including instructions encoded on storage media, for performing reduction of gradient vectors and similarly structured data that are generated in parallel, for example, on nodes organized in a mesh or torus topology defined by connections in at least two dimensions between the nodes. The methods provide parallel computation and communication between nodes in the topology.