GPU Kernel Shared Memory Initialization via Compiler Insertion
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Solution Overview
Problem
Memory initialization in CUDA programs consumes significant time and computing resources, particularly due to the need to initialize reserved shared memory only after threads have started execution.
Innovation Solution
A compiler and GPU driver system that identifies and initializes reserved shared memory by inserting at-entry and at-exit code to ensure memory is prepared before thread execution, allowing for efficient use of shared memory resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If memory initialization is performed after thread execution starts, then shared memory is accessible to threads, but memory initialization time and computing resources are significantly consumed
Solution Approach 1:
The patent applies preliminary action by inserting initialization code at the entry point of GPU kernels, ensuring that shared memory is initialized before threads begin execution. This is achieved through the compiler analyzing kernel code and automatically generating initialization sequences that execute first, eliminating the need for runtime initialization delays while ensuring memory is ready for thread access.
Solution Approach 2:
The system applies self-service through automatic compiler-based insertion of initialization code without requiring manual programmer intervention. The compiler autonomously analyzes kernel functions, identifies shared memory usage, and inserts appropriate initialization sequences, making the system self-configuring and reducing overhead while ensuring proper memory preparation.
2Reliability
If manual memory initialization code is added, then shared memory is properly prepared, but device complexity and programming effort increase
Solution Approach 1:
The compiler system applies self-service by automatically analyzing kernel code, identifying shared memory variables, and generating initialization code without programmer intervention. This autonomous analysis and code generation eliminates manual programming complexity while ensuring proper memory preparation, making the system adaptive and self-configuring.
Solution Approach 2:
The patent applies universality through a general-purpose compiler mechanism that handles various types of shared memory initialization scenarios through a unified code insertion approach. The same compiler pass and code generation framework handles different kernel types and memory access patterns, providing a universal solution that reduces programming complexity across diverse use cases.
Data Source
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AI summary
Apparatuses, systems, and techniques to compile and modify software programs. In at least one embodiment, a software program is to be modified to initialize information to be used by one or more application programming interfaces (APIs).