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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
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A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
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    Area of Science:

    • Computer Graphics
    • Computational Geometry
    • Performance Optimization

    Background:

    • Optimizing acceleration structures (e.g., Bounding Volume Hierarchies - BVHs) is crucial for efficient ray tracing.
    • Parameter interdependencies and context sensitivity complicate manual optimization.
    • Previous autotuning methods show promise but can be inefficient for complex parameter spaces.

    Purpose of the Study:

    • To analyze the behavior and impact of acceleration structure parameters on building and rendering times.
    • To develop an efficient autotuning method for ray tracing acceleration structures.
    • To introduce a hybrid prediction and online autotuning approach for faster convergence.

    Main Methods:

    • Detailed analysis of parameter interdependence and context sensitivity (scene, viewpoint).
    • Application of autotuning, previously successful on kD-trees, to BVHs.
    • Development of a hybrid model-based prediction and online autotuning method.

    Main Results:

    • Identified parameter interdependence and context sensitivity, enabling targeted optimization.
    • Online autotuning achieved up to 11% median speedup over literature configurations.
    • The prediction model reached 95% of maximum autotuner speedup while reducing overhead by 90%.

    Conclusions:

    • Hybrid online autotuning provides an efficient and effective solution for optimizing ray tracing acceleration structures.
    • The proposed method enables always-on tuning, significantly improving rendering performance.
    • Context-sensitive parameter analysis allows for more focused and efficient optimization strategies.