Computation Graph Partitioning for ML Pipeline Execution
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Solution Overview
Problem
Manual partitioning of computation graphs in execution pipelines for machine learning models is time-consuming, error-prone, and leads to inefficiencies due to uneven computational load between stages, especially when dealing with large-scale models and limited memory resources.
Innovation Solution
A computer-implemented method using a constraint solver to automatically partition a computation graph into ordered stages of an execution pipeline, balancing execution costs by encoding various factors into constraints, such as execution cycles, energy use, and memory requirements, thereby optimizing the distribution of computational tasks across machines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual partitioning is used to divide computation graph into stages, then partitioning can be performed with simple tools, but the process is time-consuming and leads to uneven computational load distribution
Solution Approach 1:
The system automatically partitions the computation graph by representing it as a directed acyclic graph and using a constraint solver to compute optimal stage divisions. The partitioning is performed self-service through algorithmic automation rather than manual intervention, with the constraint solver automatically balancing computational load and determining stage boundaries based on the graph structure and execution costs.
Solution Approach 2:
The patent transforms the partitioning problem into a constraint satisfaction problem by changing parameters such as execution cost, memory requirements, and computational load into formal constraints. The constraint solver processes these parameter transformations to generate optimized partitions that would be impossible to achieve manually, resolving the contradiction between ease of manual partitioning and productivity.
2Device complexity
If manual partitioning is used, then the process requires minimal computational resources, but it leads to inefficient execution due to uneven load distribution
Solution Approach 1:
The patent replaces manual mechanical partitioning processes with an automated constraint solving system. Instead of manually analyzing and dividing computation graphs, the system uses algorithmic constraint solvers that automatically process the computation graph representation, apply constraints based on execution costs and memory requirements, and generate optimal partitions. This substitution of mechanical manual work with automated computational processes resolves the contradiction between system complexity and execution efficiency.
3Adaptability or versatility
If more stages are created in the execution pipeline, then parallelization and scalability are improved, but the complexity of managing and balancing computational load increases
Solution Approach 1:
The patent segments the computation graph into discrete stages based on dependency relationships and execution costs. By representing the computation as a directed acyclic graph and identifying natural segmentation points, the system creates manageable stages that can be independently executed in parallel. This segmentation approach enables scalability while keeping management complexity controlled through systematic division rather than arbitrary splitting.
Solution Approach 2:
The constraint solver dynamically adjusts the number and configuration of stages based on the specific computation graph and available resources. Rather than using a fixed number of stages, the system adaptively determines the optimal stage division by processing constraints related to execution costs, memory requirements, and computational load. This dynamic adaptation enables scalability while managing complexity through automated adjustment rather than manual configuration.
Data Source
AI summary
A computation graph of a machine learning model is accessed from memory and a constraint solver is used to compute a partition of the computation graph into ordered stages of an execution pipeline. In use, when inference or training of the machine learning model takes place by executing the pipeline, execution cost of the stages are balanced according to the computed partition.