Reduction Server for Distributed Training Latency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning training methods face challenges in efficiently processing large datasets across multiple machines, leading to bottlenecks in communication and resource management, particularly with the allreduce operation, which incurs high network traffic and latency.
Innovation Solution
The introduction of a Reduction Server architecture that distributes gradient computation and communication across worker and reducer processes, using virtual machines to manage data blocks and reduce the number of messages exchanged, thereby offloading computation from workers to vCPUs and utilizing bandwidth more efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If allreduce operation is used for distributed training, then gradient aggregation is achieved, but network traffic and communication latency increase significantly
Solution Approach 1:
The patent segments the gradient aggregation process by introducing separate reducer processes that handle specific portions of gradient data. Instead of all workers participating in every reduction operation, the system divides the work so that reducers process specific gradient chunks, reducing the communication overhead and network traffic required for full allreduce operations.
Solution Approach 2:
The patent introduces reducer processes as intermediary components between workers and the central coordination. These reducers act as mediators that aggregate gradients from multiple workers, reducing the direct communication burden on the network and enabling more efficient gradient aggregation with less network traffic.
2Productivity
If more virtual machines are added for distributed processing, then computing power increases, but system complexity and management overhead increase
Solution Approach 1:
The patent creates universal reducer processes that can handle multiple types of reduction operations and work with different worker configurations. These reducers serve multiple functions including gradient aggregation, synchronization, and coordination, reducing the need for specialized components and simplifying the overall system architecture despite increased VM deployment.
Solution Approach 2:
Instead of having workers directly communicate with each other or with a central coordinator for all operations, the patent inverts the communication pattern by introducing reducers that workers communicate with for aggregation tasks. This inversion simplifies worker logic and centralizes the complexity in manageable reducer processes.
3Ease of operation
If gradient aggregation is performed centrally, then coordination is simplified, but communication bandwidth requirements increase
Solution Approach 1:
The patent segments the gradient data into chunks that are processed by different reducer processes. Instead of aggregating all gradients centrally in one location, the system divides the aggregation task across multiple reducers, each handling specific gradient portions, thereby reducing the communication bandwidth required at any single point while maintaining coordinated operation.
Solution Approach 2:
The patent transitions from a single-dimensional central aggregation model to a multi-dimensional distributed reduction architecture. By introducing multiple reducers that operate in parallel across different dimensions of the gradient space, the system reduces the communication burden on any single communication channel while maintaining overall coordination.
Data Source
AI summary
A data processing system, that includes: one or more host processing devices, the one or more host processing devices may be configured to support instantiation of a plurality of virtual machines such that a first set of virtual machines run one or more worker processes, each worker process operating on a respective data set to produce a respective gradient. The host processing devices may be configured to support instantiation of a second set of virtual machines running one or more reducer processes that operate on each respective gradient produced by each worker process to produce an aggregated gradient. The one or more reducer processes may cause the aggregated gradient to be broadcasted to each worker process.