Shared-Memory Parameter Sharing for Multi-Learner Training
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Solution Overview
Problem
Existing machine learning systems face inefficiencies in parameter sharing and gradient communication between learner processors, leading to high bandwidth consumption and suboptimal model accuracy.
Innovation Solution
A system where multiple learner processors receive global parameters from a server, apply them to mini-batches of data, generate gradients, and share these gradients to update the parameters, reducing bandwidth consumption by using shared memory for parameter and gradient storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If global parameters are shared across multiple learner processors through network communication, then model training can be distributed and parallelized, but network bandwidth consumption increases and training efficiency decreases
Solution Approach 1:
The patent merges the parameter storage function into a shared memory resource that is locally accessible to all learner processors. Instead of each processor having separate parameter copies or communicating through the network, all processors share a common parameter storage location in local memory, eliminating network communication for parameter access and reducing bandwidth consumption while maintaining parallel training capability
Solution Approach 2:
The patent introduces a shared memory buffer as an intermediary between the parameter server and learner processors. This buffer acts as a local cache that decouples the network communication from the parameter access operations, allowing processors to retrieve parameters from local memory without network latency while the parameter server updates parameters asynchronously
2Speed
If each learner processor maintains separate copies of global parameters, then parameter access is fast and independent, but memory usage increases and parameter consistency becomes difficult to maintain
Solution Approach 1:
The patent combines multiple separate parameter copies into a single shared parameter storage location in local memory. This eliminates redundant storage of identical parameter data across processors while maintaining fast access speeds through shared memory architecture that provides low-latency access to all learner processors simultaneously
Solution Approach 2:
The shared parameter storage structure serves multiple functions: it acts as a common data source for all learner processors, a cache for recently updated parameters, and a coordination mechanism for parameter synchronization. This multi-functional design reduces overall memory requirements while supporting parallel training operations
3Measurement precision
If gradient updates are communicated frequently between processors and parameter server, then model accuracy improves through better gradient aggregation, but communication overhead and training time increase
Solution Approach 1:
The patent implements periodic gradient synchronization where learner processors accumulate gradients locally and perform batched updates to the shared parameters at regular intervals rather than after every mini-batch. This periodic communication pattern reduces the frequency of parameter server interactions while maintaining model accuracy through accumulated gradient information
Solution Approach 2:
The patent performs preliminary gradient accumulation and consolidation at the learner processors before communicating with the parameter server. By pre-processing and aggregating gradients locally, the system reduces the volume of data that needs to be communicated and processed by the parameter server, thereby reducing communication overhead and training time
Data Source
AI summary
A machine receives a first set of global parameters from a global parameter server. Multiple learner processors in the machine execute an algorithm that models an entity type using the first set of global parameters and a mini-batch of data known to describe the entity type. The machine generates a consolidated set of gradients that describes a direction for the first set of global parameters in order to improve an accuracy of the algorithm in modeling the entity type when using the first set of global parameters and the mini-batch of data. The machine transmits the consolidated set of gradients to the global parameter server. The machine then receives a second set of global parameters from the global parameter server, where the second set of global parameters is a modification of the first set of global parameters based on the consolidated set of gradients.