Cross-thread register sharing for GPU matrix multiplication
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Solution Overview
Problem
Current graphics processing units (GPUs) face limitations in efficiently processing general-purpose computations and machine-learning operations due to the lack of fine-grained synchronization and register sharing across threads, which hampers performance in tasks like matrix multiplication.
Innovation Solution
Implementing cross-thread register sharing and named synchronization barriers in the graphics processing unit (GPU) to enable efficient synchronization and data sharing among threads during matrix multiplication operations, allowing for improved parallel processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cross-thread register sharing is implemented, then processing efficiency and throughput are improved, but device complexity increases
Solution Approach 1:
The patent merges register resources across multiple threads by implementing a shared register file that can be accessed by multiple thread contexts simultaneously. This allows threads to share registers without each thread requiring its own dedicated register set, thereby improving processing efficiency while controlling device complexity through resource consolidation.
Solution Approach 2:
The register file is designed to serve multiple functions and multiple threads simultaneously. A single register file can be accessed by any thread context, making it a universal resource that eliminates the need for separate register files for each thread, thus improving productivity without proportionally increasing device complexity.
2Productivity
If fine-grained synchronization is implemented, then matrix multiplication performance is improved, but device complexity increases
Solution Approach 1:
The patent implements fine-grained synchronization by dividing the computation into smaller segments that can be synchronized independently. Named synchronization barriers are placed at specific points in the computation pipeline, allowing threads to synchronize only when necessary rather than requiring complex global synchronization mechanisms throughout the entire system.
Solution Approach 2:
Named synchronization barriers act as intermediary mechanisms that facilitate coordination between threads without requiring direct complex interaction between all thread pairs. These barriers provide a simplified interface for synchronization, improving matrix multiplication performance while avoiding the need for complex peer-to-peer synchronization logic.
3Productivity
If data sharing among threads is enabled, then throughput is improved, but device complexity increases
Solution Approach 1:
The patent combines data storage resources by implementing a shared register file that can be accessed by multiple threads simultaneously. This merging of data storage resources allows efficient data sharing among threads involved in matrix multiplication, improving throughput while avoiding the complexity of multiple separate storage structures.
Solution Approach 2:
The patent changes the accessibility parameters of the register file from thread-private to thread-shared. By modifying the access control parameters to allow multiple threads to access the same register file with appropriate synchronization, data sharing is enabled efficiently without adding complex data structures or memory systems.
Data Source
AI summary
An apparatus to facilitate cross-thread register sharing for matrix multiplication compute is disclosed. The apparatus includes matrix acceleration hardware comprising a plurality of data processing units, wherein the respective plurality of data processing units are to: receive a decoded instruction for a first thread having a first register space, wherein the decoded instruction is for a matrix multiplication operation and comprises an indication to utilize a second register space of a second thread for an operand of the decoded instruction for the first thread; access the second register space of the second thread to obtain data for the operand of the decoded instruction; and perform the matrix multiplication operation for the first thread using the data for the operand from the second register space of the second thread.