Computation Graph Kernel Fusion for GPU Execution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning frameworks face inefficiencies in executing computation graphs due to eager execution of operations, which prevents optimizations across operations and requires significant reimplementation to port applications to different architectures like GPUs and CPUs, limiting the use of GPUs in machine learning applications.
Innovation Solution
A methodology that combines operations across stages of a computation graph to generate specialized code for execution on GPUs and CPUs, allowing for efficient execution and optimization without significant changes to existing frameworks, by representing nodes as data structures and traversing them to form kernels of operations that can be executed on various architectures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If operations in computation graphs are executed eagerly (one by one), then the execution is simple to implement, but the execution efficiency is low and optimizations across operations cannot be performed
Solution Approach 1:
The system performs preliminary analysis of the computation graph to identify fusion opportunities before execution. By pre-processing the graph to detect compatible operations and prepare fusion plans, the system enables efficient parallel execution without sacrificing implementation simplicity. This preliminary preparation allows the runtime system to execute fused operations optimally.
Solution Approach 2:
The invention introduces an intermediary layer (the fusion analysis system) between the computation graph definition and execution. This intermediary automatically analyzes operation compatibility, generates fusion plans, and transforms the graph without requiring changes to the original eager execution model or user code, thus maintaining ease of implementation while achieving optimization.
2Productivity
If operations are fused across different stages of the computation graph, then execution efficiency is improved, but the code complexity increases
Solution Approach 1:
The fusion analysis is segmented into distinct phases: graph traversal to identify operation sequences, compatibility analysis to determine fusion feasibility, and code generation to produce optimized kernels. This segmentation manages complexity by breaking down the fusion process into manageable, independent steps that can be implemented and maintained separately.
Solution Approach 2:
The system performs self-service by automatically analyzing the computation graph and generating fusion code without requiring manual intervention or complex configuration from developers. The fusion analysis system autonomously identifies optimization opportunities and produces the necessary code transformations, keeping the implementation complexity contained within the optimization layer rather than propagating to user code.
3Productivity
If machine learning applications are ported to GPUs, then performance is significantly improved, but significant reimplementation effort is required
Solution Approach 1:
The fusion analysis system provides a universal interface that works across different computing architectures (CPU, GPU, and other accelerators). By analyzing the computation graph in a architecture-agnostic manner and generating code that can be targeted to multiple backends, the system enables porting to GPUs without requiring significant reimplementation. The same fusion analysis infrastructure serves multiple architecture types.
Solution Approach 2:
The system creates a copy of the computation graph for analysis purposes, allowing the fusion analysis to operate on this analytical copy while the original graph remains unchanged. This copying approach enables the generation of optimized GPU code from the same source computation graph without modifying the original application code, thus achieving porting with minimal reimplementation effort.
4Productivity
If computation graphs are optimized for specific architectures, then execution performance on that architecture is improved, but the system loses adaptability to other architectures
Solution Approach 1:
The fusion analysis system is designed to be dynamic and configurable, allowing it to adapt its analysis and code generation strategies based on the target architecture. The system can adjust fusion criteria, kernel generation approaches, and optimization techniques depending on whether the target is CPU, GPU, or other accelerators, thus maintaining both specialized performance optimization and broad architecture adaptability.
Data Source
AI summary
A computation graph is accessed. In the computation graph, operations to be performed are represented as interior nodes, inputs to the operations are represented as leaf nodes, and a result of the operations is represented as a root. Selected sets of the operations are combined to form respective kernels of operations. Code is generated execute the kernels of operations. The code is executed to determine the result.