Tensor-Based GPU Memory Optimization for Deep Learning
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Solution Overview
Problem
Deep learning systems face challenges in managing GPU memory efficiently due to hardware heterogeneity, varying computation times, and the limitations of static memory optimization methods, which are not adaptable to new neural network architectures or frameworks that do not rely on computation graphs.
Innovation Solution
A tensor-based optimization method for GPU memory management that dynamically tracks tensor access information during training, performs passive memory swapping between CPU and GPU during the first iteration, and adjusts memory management decisions based on operational feedbacks in subsequent iterations to optimize memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static analysis of computing graph is used for memory management, then memory optimization can be performed based on layer characteristics, but it cannot adapt to hardware heterogeneity and varying computation times
Solution Approach 1:
The patent transitions from static layer-based memory management to dynamic tensor-based memory management. The system dynamically tracks tensor access patterns, computation times, and memory usage during actual execution, allowing memory optimization decisions to adapt to hardware heterogeneity and varying computation times in real-time rather than relying on predetermined static analysis.
Solution Approach 2:
The patent implements feedback mechanisms by monitoring actual tensor access information, computation times, and memory usage during training iterations. This feedback is used to dynamically adjust memory management decisions, such as when to swap tensors between CPU and GPU memory, enabling the system to adapt to hardware variations and optimize performance based on observed behavior rather than static predictions.
2Productivity
If passive memory swapping is performed during first iteration to collect access information, then dynamic optimization decisions can be made in subsequent iterations, but additional time overhead is incurred during the first iteration
Solution Approach 1:
The patent performs preliminary passive memory swapping during the first iteration to collect tensor access information and computation time data before optimizing memory management for subsequent iterations. This preliminary action incurs time overhead initially but enables more efficient memory management later, improving overall training productivity across multiple iterations.
Solution Approach 2:
The system uses its own execution data from the first iteration to automatically optimize its memory management for subsequent iterations without requiring external intervention. The collected access information is used to make self-adjusting memory decisions, reducing manual configuration and enabling the system to improve its own performance automatically.
3Quantity of substance
If tensor-based dynamic tracking is implemented, then memory usage can be optimized based on actual access patterns, but system complexity increases compared to static layer-based methods
Solution Approach 1:
The patent extracts and tracks only the essential tensor access information needed for memory optimization decisions, such as access patterns, computation times, and memory usage. By focusing on extracting only the critical data required for optimization rather than monitoring all system activities, the patent reduces the complexity overhead of the tracking system while maintaining effective memory optimization.
Data Source
AI summary
The present disclosure relates to a tensor-based optimization method for GPU memory management of deep learning, at least comprising steps of: executing at least one computing operation, which gets tensors as input and generates tensors as output; when one said computing operation is executed, tracking access information of the tensors, and setting up a memory management optimization decision based on the access information, during a first iteration of training, performing memory swapping operations passively between a CPU memory and a GPU memory so as to obtain the access information about the tensors regarding a complete iteration; according to the obtained access information about the tensors regarding the complete iteration, setting up a memory management optimization decision; and in a successive iteration, dynamically adjusting the set optimization decision of memory management according to operational feedbacks.