GPU Memory Reconfiguration with Mixed Pages and Cache Bank Shifts
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Solution Overview
Problem
Current graphics processing units (GPUs) face challenges in dynamically reconfiguring memory to optimize performance across various operations, such as graphics processing and machine learning tasks, due to limitations in cache management and memory allocation strategies.
Innovation Solution
The implementation of a dynamic reconfiguration method for cache memory bank assignments based on hardware statistics, enabling virtual memory address translation using mixed page sizes and creating heterogeneous processing systems with near and far regions in the cache hierarchy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed function computational units are used to process graphics data, then processing efficiency for specific operations is improved, but adaptability to different operations and workloads deteriorates
Solution Approach 1:
The patent implements dynamic reconfiguration of cache memory bank assignments based on detected workload types. The system transitions from static cache allocation to dynamic allocation that adapts to different operational modes (graphics processing vs. machine learning tasks), allowing the same hardware to optimize performance across diverse workloads without requiring separate fixed-function units for each operation type.
2Productivity
If cache memory is dedicated to specific operations, then performance for those operations is improved, but memory utilization efficiency deteriorates when workloads change
Solution Approach 1:
The system changes the parameters of cache memory allocation dynamically based on workload detection. When a workload type is detected (graphics or machine learning), the system reconfigures which cache memory banks are assigned to which processing units, thereby optimizing memory utilization efficiency while maintaining high performance for the current operation type.
3Device complexity
If static memory allocation is used, then system complexity is reduced, but performance optimization across varying workloads deteriorates
Solution Approach 1:
The patent implements self-service through automatic workload detection and dynamic reconfiguration. The system automatically detects the type of workload being executed and reconfigures cache memory assignments without external intervention, maintaining low system complexity while achieving performance optimization across varying workloads through autonomous adaptation.
Data Source
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AI summary
Embodiments described herein provide techniques to enable the dynamic reconfiguration of memory on a general-purpose graphics processing unit. One embodiment described herein enables dynamic reconfiguration of cache memory bank assignments based on hardware statistics. One embodiment enables for virtual memory address translation using mixed four kilobyte and sixty-four kilobyte pages within the same page table hierarchy and under the same page directory. One embodiment provides for a graphics processor and associated heterogenous processing system having near and far regions of the same level of a cache hierarchy.