Parallel Polygon Rasterization for Structure Summation
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Solution Overview
Problem
Determining the optimal placement of shapes on a finite surface to minimize material waste is a complex computational challenge, similar to issues in three-dimensional graphics and manufacturing, where efficient space utilization and shape arrangement are crucial.
Innovation Solution
The implementation of systems and methods for polygon processing using parallel processing systems, which disaggregate structures into constituent portions, rasterize combinations of these portions, and generate a sum of the structures, improving parallelization, compute, and memory use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional serial processing methods are used to determine structure summation, then computational accuracy is maintained, but processing time and computational expense increase significantly
Solution Approach 1:
The patent divides complex structures into constituent polygons and further segments the computation into multiple independent processing threads. Each thread processes a specific combination of polygons from two structures to determine occupied cells, enabling parallel execution and significantly reducing overall computational time while maintaining accuracy
Solution Approach 2:
The system dynamically adjusts the processing approach by using a hybrid method that combines parallel thread processing for polygon combination determination with sequential processing for final result aggregation. This dynamic allocation of processing modes optimizes both speed and accuracy based on the specific computational requirements
2Measurement precision
If detailed polygon combinations are processed to ensure accurate structure summation, then measurement precision is improved, but computational complexity and resource consumption increase
Solution Approach 1:
The patent segments the complex structure summation problem into discrete polygon combination tasks. By breaking down structures into individual polygons and processing each combination separately through dedicated threads, the system achieves precise results while managing computational complexity through modular organization
Solution Approach 2:
The system uses grid representations as simplified copies of the actual geometric structures. Instead of performing complex geometric calculations on the original structures, the patent maps polygon combinations onto a discrete grid system where occupancy can be determined through simpler bitwise operations, reducing computational complexity while preserving accuracy
3Manufacturing precision
If comprehensive polygon combinations are evaluated to minimize material waste, then manufacturing precision is improved, but computational resource consumption increases
Solution Approach 1:
The patent segments the nesting optimization problem into independent polygon combination evaluations. Each processing thread handles specific polygon pairs, determining their combined occupancy on the grid. This segmentation enables parallel processing that reduces total computational energy while maintaining comprehensive evaluation of all combinations for optimal nesting solutions
Solution Approach 2:
The system processes polygon combinations in parallel threads, performing slightly more computational work simultaneously than a single sequential thread would require. By using multiple threads to evaluate combinations concurrently, the system achieves comprehensive evaluation with reduced total energy consumption through efficient resource utilization
4Productivity
If parallel processing threads are used to accelerate structure summation, then productivity is improved, but memory usage and system complexity increase
Solution Approach 1:
The patent uses grid representations as lightweight copies of the geometric structures. Each processing thread works with its own grid copy to determine occupied cells, avoiding the need for complex shared memory structures. This approach enables parallel processing with minimal memory overhead, as the grid copies are compact and can be efficiently managed
Solution Approach 2:
Each processing thread focuses on a specific local task of evaluating particular polygon combinations and determining occupancy for those specific combinations. This localized approach allows threads to operate independently with minimal memory sharing requirements, reducing overall system complexity while maintaining high productivity through parallel execution
Data Source
AI summary
In various examples, systems and methods are disclosed relating to shape processing using parallel computing systems. A processor can map one or more combinations of a plurality of first polygons identified from a first structure and a plurality of second polygons identified from a second structure, to a grid comprising a plurality of cells. The processor can identify one or more occupied cells of the plurality of cells, to which the one or more combinations are mapped. The processor can output a data structure indicative of the plurality of occupied cells.