Visual Graph Computation Grouping via Spatial Extent Analysis
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Solution Overview
Problem
Existing graph-based computation systems face inefficiencies in execution, particularly in reconfiguring data parallelism, load balancing, and startup times, and struggle to adapt to varying computation resources and data characteristics, leading to suboptimal use of heterogeneous computing environments.
Innovation Solution
A visual representation of graph-based computations allows for automated determination and visualization of grouping and nesting of elements, eliminating the need for explicit user specification of bounding constructs, and uses spatial extent analysis to optimize region outlines, enabling efficient computation and accurate data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If explicit user specification of bounding constructs is required for grouping elements, then user control over grouping is improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The system automatically determines grouping and nesting of graph elements based on their spatial relationships and visual proximity, eliminating the need for explicit user specification of bounding constructs. The computation system analyzes the visual representation and autonomously identifies regions containing related elements, thereby simplifying user interaction while maintaining accurate grouping.
Solution Approach 2:
The system performs preliminary analysis of the visual representation to pre-determine grouping structures before execution. By computing spatial relationships and identifying regions in advance, the system prepares the grouping configuration proactively, reducing the operational burden on users during runtime.
2Ease of operation
If automated determination of grouping is implemented, then ease of operation is improved, but measurement precision of user intent deteriorates
Solution Approach 1:
The system provides visual feedback by displaying determined regions and groupings in the visual representation, allowing users to verify that the automated grouping matches their intent. This feedback mechanism enables users to detect and correct any misinterpretations, thereby maintaining measurement precision while benefiting from automation.
Solution Approach 2:
The grouping determination is dynamic and adaptable, allowing the system to adjust its interpretation based on visual characteristics and user interactions. The system can refine its grouping analysis in response to user feedback or changes in the visual representation, ensuring accurate interpretation of user intent.
3Speed
If processes are initiated at startup of graph execution, then readiness for computation is improved, but loss of time and use of energy worsen due to unnecessary process initialization
Solution Approach 1:
The system performs preliminary analysis and preparation of computation resources without initiating all processes at startup. By pre-configuring only the necessary components and analyzing the graph structure in advance, the system avoids unnecessary process initialization while maintaining readiness for efficient execution.
Solution Approach 2:
The system dynamically initiates processes based on actual computation needs and resource availability rather than following a fixed startup sequence. This dynamic approach allows the system to start only the necessary processes at appropriate times, reducing startup time and energy consumption while maintaining computation readiness.
4Reliability
If fixed process hosting is used, then stability of execution is improved, but adaptability to varying computation resources deteriorates
Solution Approach 1:
The system dynamically assigns and reassigns process hosting based on varying computation resources and workload characteristics. By making process hosting flexible rather than fixed, the system maintains execution stability through adaptive resource management while accommodating changes in available computing capacity.
Solution Approach 2:
The system designs process hosting to be universal and multi-functional, allowing the same hosting infrastructure to accommodate different computation workloads and resource configurations. This universal approach enables the system to adapt to varying resources while maintaining stable execution through consistent hosting principles.
Data Source
AI summary
Graph-based computation includes accepting specification information for the graph-based computation, the specification information including a plurality of graph elements, and providing a visual representation of the specification information to a user. A visual representation of one or more groups of the graph elements is determined based on the accepted specification information, including determining a spatial extend of a spatial region for at least a first group of the one or more groups, wherein the spatial extent of the spatial region is determined based at least in part on a spatial extent of each graph element of a subset of graph elements including one or more graph elements in the first group and at least one graph element out of the first group. A visual representation of spatial regions for the one or more groups is presented in conjunction with the visual information of the specification information.