Tensor Prefetching for Deep Learning Memory Latency
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Solution Overview
Problem
Existing unified memory systems for deep learning applications struggle to identify and manage memory usage patterns, leading to inefficiencies in memory allocation and prefetching.
Innovation Solution
A processor-implemented method that allocates tensors to memory for initial training iterations, generates unique information for tensors, stores pattern information for tensor and kernel allocation, and prefetches tensors based on this information to optimize subsequent training iterations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If tensors are allocated to memory for each kernel execution in deep learning training, then the deep learning application can be executed, but memory access latency increases and training performance deteriorates
Solution Approach 1:
The system performs preliminary actions by predicting which tensors will be needed for future kernel executions and prefetching them into memory before they are actually required. This advance preparation eliminates waiting time during tensor access, directly reducing memory access latency and improving training speed.
2Ease of operation
If unified memory is implemented to integrate CPU and GPU memory, then programming becomes easier with single virtual address space, but memory usage patterns become difficult to identify and manage
Solution Approach 1:
The system implements feedback mechanisms by monitoring and analyzing actual memory access patterns during kernel executions. This feedback information is used to refine predictions about future tensor access patterns, enabling the system to adaptively optimize prefetching strategies and manage unified memory more effectively.
Solution Approach 2:
The system employs self-service by automatically analyzing its own memory usage patterns and generating optimization strategies without requiring external intervention. The memory management system observes its own operations, identifies patterns, and autonomously adjusts prefetching and allocation strategies to improve performance.
3Productivity
If tensors are managed without pattern recognition, then memory allocation is simple, but memory resource utilization is inefficient
Solution Approach 1:
The system replaces mechanical, rule-based tensor management with an intelligent system that uses machine learning models to predict tensor access patterns. This substitution enables the system to automatically adapt to varying workloads and optimize memory resource utilization based on learned patterns rather than fixed allocation rules.
Data Source
AI summary
A processor-implemented method includes allocating tensors to a memory to perform an initial iteration of training of a deep learning application, wherein the initial iteration is performed through execution of a plurality of kernels and, in response to each kernel being executed, tensors corresponding to the each kernel are used to execute the kernels, storing pattern information for allocating the tensors and the kernels to the memory in the initial iteration, and prefetching the tensors based on the pattern information to perform a next iteration of training of the deep learning application.