Multi-GPU AI Model Orchestration With Hub GPU Data Flow
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Solution Overview
Problem
Coordinating efficient execution of pre-processing, inference, and post-processing on systems with multiple GPUs is challenging due to the complexity of allocating execution of individual machine learning models, configuring data transfer between GPUs, and optimizing memory allocation and data transfers, requiring significant coding expertise and knowledge of AI architecture.
Innovation Solution
A method and system for multi-GPU inference processing that includes an initialization stage where a hub GPU manages data transfers and allocates memory spaces for input and output data, and an execution stage where data is transferred efficiently between GPUs and post-processing engines, reducing latency and optimizing data flow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple GPUs are used for parallel inference processing, then processing throughput is improved, but system complexity and difficulty of coordinating data transfers between GPUs increases
Solution Approach 1:
The host system acts as an intermediary that coordinates data transfers between multiple GPUs. The host receives input data, distributes it to appropriate GPUs based on model assignments, collects output data from GPUs, and delivers final results. This mediation simplifies the complexity by centralizing coordination logic in the host rather than requiring complex peer-to-peer communication protocols between GPUs.
Solution Approach 2:
The system segments the inference workload by assigning different machine learning models to different GPUs. Each GPU independently processes specific models, allowing parallel execution. This segmentation enables throughput improvement while managing complexity through clear division of labor, where the host only needs to manage data distribution rather than complex inter-GPU computations.
2Adaptability or versatility
If frequent data transfers between host and GPUs are performed, then memory allocation flexibility is improved, but latency increases
Solution Approach 1:
The system performs preliminary actions by pre-allocating memory spaces on GPUs and preparing data transfer buffers before actual inference processing begins. During the inference stage, data is transferred efficiently between host and GPUs using these pre-prepared memory spaces, reducing the need for frequent reallocation and minimizing transfer latency. The initialization stage sets up all necessary memory structures in advance.
3Productivity
If complex coordination of pre-processing, inference, and post-processing stages is implemented across multiple GPUs, then processing efficiency is improved, but coding complexity and expertise requirements increase
Solution Approach 1:
The host system serves multiple functions: it manages data distribution to GPUs, coordinates the three processing stages (pre-processing, inference, post-processing), allocates memory spaces, and handles output aggregation. This universal coordination role simplifies the overall system architecture by consolidating control logic in a single component rather than requiring complex distributed coordination protocols across multiple GPUs and processing stages.
Data Source
AI summary
Apparatuses, systems, and frameworks for provisioning of efficient pipelines capable of multi-model inference and data processing using multiple processing units, including streaming data applications. The disclosed techniques include, during an initialization stage, assigning a plurality of machine learning models (MLMs) for execution on graphics processing units (GPUs), allocating memory space, on a hub GPU, to the plurality of MLMs, storing input data on the hub GPU before transferring the input data to other GPUs for execution. During an execution stage, output data is initially stored on GPUs that generated the output data before transferring the output data to the hub GPU.