Write Memory Access Slicing for GPU Divergence Analysis
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Solution Overview
Problem
Current techniques fail to address control-flow divergence, memory divergence, and irregular workload distribution, which impede GPU utilization and productivity in high-performance program development, particularly in large, irregularly parallel shaders, leading to challenges in debugging, program analysis, and performance tuning.
Innovation Solution
A method to modify source code representations by identifying decomposable write memory accesses, calculating slicing criteria, and generating independent representation slices to improve parallelism and locality of memory accesses, thereby enhancing GPU performance and debugging efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If source code is processed as a single monolithic unit, then simplicity of processing is maintained, but GPU utilization and productivity are impeded due to control-flow divergence and memory divergence
Solution Approach 1:
The source code is decomposed into multiple independent program slices based on memory access patterns and control flow analysis. Each slice represents a subset of operations that can be executed independently, enabling parallel processing on GPU while reducing the complexity of analyzing and optimizing each individual slice.
2Speed
If memory accesses are processed sequentially, then memory access simplicity is maintained, but performance is reduced due to poor locality and divergence
Solution Approach 1:
Memory accesses are segmented into distinct patterns (uniform, divergent, oversubscription) and each pattern is processed separately through specialized slicing criteria. This allows optimized handling of each memory access type, improving overall memory access speed while managing complexity through systematic classification.
Solution Approach 2:
Different memory access regions are assigned different slicing criteria based on their specific characteristics. Uniform accesses use one set of criteria, divergent accesses use another, and oversubscription accesses use a third. This localized optimization improves performance for each region while maintaining overall system coherence.
3Loss of time
If debuggng is performed on monolithic source code, then debugging simplicity is maintained, but time consumption increases due to lack of fine-grained analysis capability
Solution Approach 1:
The source code is divided into fine-grained program slices that can be independently analyzed and debugged. Each slice maintains the necessary context and can be processed separately, enabling faster debugging through targeted analysis while reducing the overall complexity of the debuggng process through systematic organization.
4Productivity
If parallel execution is implemented without slicing, then programming simplicity is maintained, but performance is limited due to inability to handle control-flow and memory divergence
Solution Approach 1:
The execution model segments parallel operations into distinct program slices based on control flow and memory access characteristics. This segmentation enables the GPU to efficiently handle divergence by processing independent slices in parallel, improving execution performance while managing complexity through automated slicing based on identified memory access patterns.
Data Source
AI summary
This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for modifying a representation of source code. The representation of source code may include the source code or a transformed form of the source code (e.g., an intermediate representation (IR)). A processor configured to modify the representation of source code may obtain the representation of the source code. The processor may identify that the representation comprises a first set of decomposable write memory accesses. The processor may calculate a second set of slicing criteria based on the identified first set of decomposable write memory accesses. The processor may generate a plurality of representation slices based on the representation of the source code and the calculated second set of slicing criteria. The processor may output an indicator of the generated plurality of representation slices.