Distributed Neural Network Abstraction for Scalable GPU Training
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Solution Overview
Problem
Current graphics processing units (GPUs) face limitations in efficiently handling diverse data processing tasks beyond traditional graphics operations, particularly in parallel computing environments, due to fixed function computational units and suboptimal pipelining techniques.
Innovation Solution
Implementing a GPU architecture with programmable processing units and SIMT architectures, coupled with a scheduler for dynamic workload distribution across processing clusters, enabling efficient parallel processing of graphics and general-purpose computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed function computational units are used in GPUs, then traditional graphics operations can be performed efficiently, but the GPU cannot efficiently handle diverse data processing tasks beyond graphics operations
Solution Approach 1:
The patent implements a unified computational unit architecture where the same processing units can execute both traditional graphics operations and general-purpose computing tasks including machine learning workloads. This is achieved through a programmable execution model where processing elements can be dynamically configured to perform different types of computations, eliminating the need for separate fixed-function hardware for each task type while maintaining high performance across diverse workloads.
2Adaptability or versatility
If programmable processing units and SIMT architectures are implemented, then diverse workloads including machine learning operations can be handled, but device complexity increases
Solution Approach 1:
The patent divides the processing architecture into multiple independent processing clusters, each containing processing elements that can be independently configured and executed. This segmentation allows the system to handle diverse workloads by distributing different task types across different clusters while maintaining a relatively simple individual cluster design. Each cluster can be independently programmed and managed, reducing the complexity of controlling the entire system.
Solution Approach 2:
The patent implements dynamic configuration of processing elements where the same hardware resources can be reconfigured on-the-fly to match the requirements of different workloads. This dynamic adaptability is achieved through a programming model that allows runtime specification of computational kernels and their parameters, enabling the system to optimize its structure for the current task without requiring complex static design decisions.
3Productivity
If pipelining techniques are used to process graphics data in parallel, then processing throughput is improved, but the fixed function units limit efficiency for non-graphics parallel computing tasks
Solution Approach 1:
The patent creates a unified processing pipeline that can handle both graphics and non-graphics workloads efficiently. The same pipeline stages and processing elements that optimize graphics throughput are also capable of executing general-purpose computing tasks including machine learning operations, achieving high throughput across diverse task types without requiring separate specialized pipelines.
Data Source
AI summary
One embodiment provides for a non-transitory machine readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform operations comprising providing an interface to define a neural network using machine-learning domain specific terminology, wherein the interface enables selection of a neural network topology and abstracts low-level communication details of distributed training of the neural network.