Cache Bank-Aware Thread Scheduling for GPU Load Conflicts
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Solution Overview
Problem
Current graphics processing units face inefficiencies due to cache bank conflicts and dependencies that hinder performance in parallel processing architectures, particularly in SIMT architectures where threads accessing the same cache bank can lead to bottlenecks and reduced throughput.
Innovation Solution
Implementing bank aware thread scheduling and early dependency clearing techniques to arbitrate threads based on cache bank access and clear dependencies before data is loaded, reducing conflicts and improving instructions per clock (IPC).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple threads access the same cache bank in parallel, then processing throughput is increased, but cache bank conflicts occur leading to bottlenecks and reduced performance
Solution Approach 1:
The system performs preliminary actions by clearing cache dependencies before data is loaded into the cache banks. The dependency clearing unit proactively removes dependencies associated with cache load operations before the actual data arrival, preventing future conflicts and ensuring smooth parallel thread execution without bottlenecks
2Productivity
If threads are scheduled without considering cache bank access patterns, then scheduling simplicity is maintained, but cache bank conflicts increase reducing instructions per clock
Solution Approach 1:
The thread scheduling system is segmented into multiple independent units: a thread arbitration unit that selects threads based on simple criteria, and a dependency clearing unit that handles dependency management separately. This segmentation allows the arbitration logic to remain simple while the dependency clearing unit proactively manages cache bank conflicts, improving instructions per clock without significantly increasing overall scheduling complexity
Solution Approach 2:
A dependency clearing unit acts as an intermediary between thread execution and cache bank access. This intermediary proactively clears dependencies before data loads, mediating between the simple thread arbitration mechanism and the complex cache bank access patterns, thereby improving instructions per clock while maintaining relative simplicity in the core scheduling logic
3Productivity
If dependencies are cleared after data is loaded, then processing flow is simpler, but operational delays increase reducing system performance
Solution Approach 1:
The system performs the dependency clearing action preliminarily, before the data is actually loaded into the cache banks. By proactively removing dependencies in advance of data arrival, the system eliminates waiting time and operational delays, thereby improving overall system performance without adding complex processing steps
Data Source
AI summary
Bank aware thread scheduling and early dependency clearing techniques are described herein. In one example, bank aware thread scheduling involves arbitrating and scheduling threads based on the cache bank that is to be accessed by the instructions to avoiding bank conflicts. Early dependency clearing involves clearing dependencies for cache loads in a scoreboard before the data is loaded. In early dependency clearing for loads, delays in operation can be reduced by clearing dependencies before data is required from the cache.