GPU Memory Compression for Deep Learning Model Layers
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Solution Overview
Problem
Current GPU devices have limited memory capacity, which restricts the processing of large datasets and deeper models in deep learning applications, leading to inefficient data management and increased CPU-GPU communication, resulting in performance bottlenecks and high power consumption.
Innovation Solution
Implementing dynamic GPU memory allocation and data compression/decompression protocols to optimize memory usage, allowing for proactive compression of data from previous layers and decompression of next layers, thereby reducing memory requirements and bandwidth usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If GPU memory capacity is increased to handle larger datasets and deeper models, then processing capability is improved, but device complexity and cost increase
Solution Approach 1:
The patent segments the deep learning model into multiple functional layers and processes them sequentially, with only the current and next layers loaded in GPU memory at any given time. This segmentation allows the system to handle deep models without requiring all model data to be stored in GPU memory simultaneously, thus resolving the contradiction between memory capacity and device complexity.
Solution Approach 2:
The patent implements preliminary decompression of the next functional layer data before it is needed for processing. By pre-decompressing the next layer from compressed storage, the system prepares data in advance, reducing the need for large memory capacity while maintaining processing efficiency.
2Quantity of substance
If data is stored in compressed format to reduce memory usage, then memory requirements are reduced, but processing speed decreases due to decompression overhead
Solution Approach 1:
The system performs preliminary decompression of the next functional layer data before it is needed for processing. This advance preparation allows the actual processing to proceed at full speed without decompression delays, as the data is already in the required format when needed.
Solution Approach 2:
The patent maintains continuous useful action by overlapping compression of the current layer with decompression of the next layer. This pipeline approach ensures that memory usage is minimized while processing continues uninterrupted, resolving the contradiction between memory efficiency and processing speed.
3Productivity
If more data is loaded into GPU memory for processing, then computational capability is improved, but CPU-GPU communication burden increases
Solution Approach 1:
The patent segments data management by processing only the current and next functional layers in GPU memory at any time, rather than loading all data. This segmentation dramatically reduces the amount of data that needs to be transferred between CPU and GPU, resolving the contradiction between computational capability and communication time.
Solution Approach 2:
The system performs preliminary decompression of the next layer data on the GPU before it is needed, reducing the burden on CPU-GPU communication. By preparing data in advance on the GPU, the system minimizes frequent transfers between CPU and GPU, thus reducing communication time while maintaining high computational capability.
Data Source
AI summary
Systems and methods are provided for optimizing GPU memory allocation for high-performance applications such as deep learning (DL) computing. For example, a DL task is executed using GPU resources (GPU device and GPU memory) to process a DL model having functional layers that are processed in a predefined sequence. A current functional layer of the DL model is invoked and processed using the GPU device. In response to the invoking, a data compression operation is performed to compress data of a previous functional layer of the DL model, and store the compressed data in the GPU memory. Responsive to the invoking, compressed data of a next functional layer of the DL model is accessed from the GPU memory and a data decompression operation is performed to decompress the compressed data for subsequent processing of the next functional layer of the DL model by the GPU device.