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In modern enterprise operations, data is a critical resource, and image data plays a critical role in all areas. From MRI and CT scan images in the field, to subsurface structure images in geological exploration, to product inspection images in manufacturing, image data provides valuable information for decision support and business innovation. However, with the increase of data size, traditional image processing methods begin to show some problems such as slow processing speed and insufficient real-time performance.

Based on this, Micro Beauty Holography (NASDAQ:WIMI) has developed ParallelMorphBoost technology for morphological reconstruction based on graphics processors, bringing a new solution to the field of enterprise image processing. The core goal of this technology is to achieve efficient acceleration of the image morphology reconstruction process by leveraging the parallel computing power of the graphics processor (GPU) and the parallel heap cluster data structure designed for the GPU.

The core principle of WIMI Parallel MorphBoost technology is to make full use of the parallel computing power of GPU. Traditional morphological reconstruction requires iterative operations on a large number of pixels, and the operations on each pixel are independent of each other. By distributing these operations to multiple computing cores on the GPU for simultaneous processing, ParallelMorphBoost enables multitask parallel computing, and GPUs can take advantage of powerful parallel computing platforms to significantly accelerate processing.

GPUs have a large number of parallel computing cores, also known as CUDA cores. These cores can perform multiple computational tasks simultaneously, and each core can perform specific operations independently. Traditional CPU is difficult to process a large number of computing tasks at the same time when faced with large-scale data because of the small number of cores. However, GPUs can process morphological reconstruction operations for multiple pixels at the same time. In reconstruction, the morphological operations of each pixel are independent of each other, i.e. the processing of each pixel is independent of the results of other pixels. This property makes morphological operations ideal for parallel computing. ParallelMorphBoost technology divides operations on a large number of pixels into multiple tasks and assigns these tasks to different CUDA cores for parallel execution on the GPU. This approach allows multiple pixels to be processed at the same time, significantly speeding up the overall processing.

Morphological reconstruction is an iterative process in which the labeled image is updated at each iteration until the labeled image no longer changes. With efficient parallel computing by GPUs, each iteration converges faster, thus reducing the number of iterations. This means that ParallelMorphBoost not only speeds up processing in a single iteration, but also further speeds up the overall morphological reconstruction by reducing the total number of iterations.

With this GPU-based parallel computing approach, WIMI's ParallelMorphBoost technology enables traditional morphological reconstruction processes to be redesigned in parallel computing environments to achieve efficient acceleration in large-scale image data processing. GPU's parallel computing core, high memory bandwidth, and independence of morphological operations make this technology have great potential in image processing.

In order to fully utilize the computing potential of GPUs, WIMI's ParallelMorphBoost technology uses a special data structure: Parallel Heap Cluster. Multiple pixel manipulation tasks in morphological reconstruction operations are assigned to multiple compute cores on the GPU to execute simultaneously. Because each pixel operation is independent, GPUs are able to process multiple pixel operations in parallel at the same time, significantly speeding up the overall image processing. This parallel computing approach can effectively cope with the processing needs of large-scale image data and provide more efficient solutions for enterprises. This data structure is optimized for GPUs to efficiently manage and process large amounts of data in parallel environments. ParallelMorphBoost technology ensures maximum efficiency of data processing on GPUs by converting morphological operations into tasks suitable for GPU parallel processing.

Parallel heap cluster design: In traditional image processing, the choice of data structure is critical to performance. WIMI's ParallelMorphBoost technology makes full use of GPU's high memory bandwidth and parallel computing power through parallel heap cluster design. Parallel heap clusters are GPU-friendly data structures that support data management and processing in parallel environments.

Data partitioning and task allocation: ParallelMorphBoost technology divides morphological operations into multiple tasks and assigns them to different compute cores on the GPU for simultaneous processing. Each task corresponds to an operation of a pixel, and these operations are independent of each other. The advantage of the parallel heap cluster data structure is that it can efficiently manage these tasks and, through proper task scheduling, allow the compute core on the GPU to perform operations in full parallel.

Parallel computation and synchronization of results: On the GPU, each compute core performs morphological operations independently. This parallel computing method effectively reduces the time overhead of iterative process. However, at the end of each iteration, the results need to be synchronized to update the marker image. ParallelMorphBoost technology ensures data consistency between different computing cores through efficient synchronization mechanisms.

Iterative convergence and end result: Morphological reconstruction is an iterative process, with each iteration updating the labeled image. With GPU acceleration, the number of iterations can be significantly reduced, thus speeding up the entire morphological reconstruction process. When the marked image does not change any more, the algorithm converges and the final result is obtained.

Through the above technical logic, Micro-American Holography (NASDAQ:WIMI) ParallelMorphBoost technology can efficiently decompose morphological reconstruction operations into tasks suitable for GPU parallel processing, giving full play to GPU computing power. This parallel computing approach significantly increases the speed of processing large-scale image data, thus meeting the needs of modern enterprises for efficient image processing. The implementation logic of ParallelMorphBoost technology lies in transforming morphological reconstruction operations into parallel tasks through parallel heap cluster data structure, and processing large-scale image data efficiently through GPU parallel computing power. The innovation of this technology lies in combining the advantages of GPU with the data structure of parallel heap cluster, providing a new and efficient acceleration solution for enterprise image processing.

In addition, WIMI's ParallelMorphBoost technology can be applied to a wide range of fields. In the imaging field, this technology can speed up the analysis of image information of tumors, organs and so on, thus providing faster support for diagnosis and treatment. Parallel MorphBoost technology can accelerate underground structure analysis in geological exploration and provide more accurate data support for resource development. In the field of manufacturing quality inspection, this technology can quickly detect product defects and measure size under real-time requirements, improving quality inspection efficiency. With the continuous evolution and optimization of technology, it can be predicted that ParallelMorphBoost technology will show its potential in more fields, accelerate the image processing of enterprises, and promote the rapid development of innovation.

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