Dataflow Graph Optimization for Redundant Operation Removal
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Solution Overview
Problem
Conventional techniques for automatically generating dataflow graphs result in inefficient execution, requiring significant computational resources and time due to redundant operations, serial processing when parallel is possible, unnecessary data processing, and duplication, among other inefficiencies.
Innovation Solution
Implement dataflow graph optimization techniques that apply various optimization rules to reduce computational resources by removing redundant operations, performing strength reductions, combining operations, and deduplicating nodes, thereby generating an updated dataflow graph that is more efficient in execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional techniques for automatically generating dataflow graphs are used, then data processing operations can be performed, but execution is inefficient and requires significant computational resources due to redundant operations, serial processing when parallel is possible, unnecessary data processing, and duplication
Solution Approach 1:
The patent extracts and removes redundant operations, unnecessary data processing, and duplicate nodes from the dataflow graph through optimization rules. This eliminates wasted computational steps while preserving the essential data processing functionality, directly improving execution efficiency and reducing computational resource consumption.
Solution Approach 2:
The patent combines multiple serial operations that can be executed in parallel by reorganizing the dataflow graph structure. This merging of operations into parallel execution paths increases productivity by utilizing multiple processors simultaneously, while reducing the overall execution time and computational resource requirements.
2Speed
If conventional dataflow graph generation is used, then data processing can be performed, but serial processing is used when parallel processing is possible
Solution Approach 1:
The patent dynamically reorganizes the dataflow graph structure to enable parallel processing where possible. By analyzing operation dependencies and reconfiguring the graph to allow concurrent execution of independent operations, the system achieves faster processing speeds while maintaining manageable structural complexity through systematic optimization rules.
3Loss of energy
If conventional dataflow graph generation is used, then data processing operations are performed, but redundant operations and duplication occur
Solution Approach 1:
The patent identifies and discards redundant operations, unnecessary data processing steps, and duplicate nodes from the dataflow graph. By removing these wasteful elements while preserving the essential processing logic, the system reduces computational resource loss and improves overall execution efficiency.
Data Source
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AI summary
At least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform: obtaining an automatically generated initial dataflow graph, the initial dataflow graph comprising a first plurality of nodes representing a first plurality of data processing operations and a first plurality of links representing flows of data among nodes in the first plurality of nodes; and generating an updated dataflow graph by iteratively applying dataflow graph optimization rules to update the initial dataflow graph, the updated dataflow graph comprising a second plurality of nodes representing a second plurality of data processing operations and a second plurality of links representing flows of data among nodes in the second plurality of nodes, wherein the second plurality of nodes includes a node representing a first data processing operation and another node representing the second data processing operation, the generating comprising: identifying a first portion of the initial dataflow graph to which to apply a first optimization rule at least in part by identifying a first node representing a first data processing operation that commutes with a second data processing operation represented by a second node connected to the first node; and applying the first optimization rule to the first portion of the initial dataflow graph.