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→Optimizing Image Processing using Intel's Integrated Performance Primitives, Thread Building Blocks, and OpenMP w/ Comparison
We had originally intended to use Intel's Data Analytics Acceleration Library, but as work progressed on the project we realized that the library was not well suited to our needs. Intel's oneAPI DAL (Data Analytics Library) was our chosen library to complete this project. However, due to changes to our team and the nature of the project we wanted to pursue, we decided to use Intel IPP (Integrated Performance Primitives) instead of DAL.
DAL is a robust and capable library for data analytics and machine learning. It is designed with linear algebra and statistical operations in mind.
DAL offers parallelization capabilities but is not explicitly optimized for image processing operations. Image processing involves working with large arrays of pixel data, which requires specialized data structures and memory access patterns. DAL's focus on linear algebra and statistical operations may not be well-suited to these procedures. Other libraries specifically designed for image processing, such as OpenCV, offer better performance because they can take advantage of GPUs' parallel processing for Image processing. Also, They offer a more significant number of features than DAL. Libraries specifically designed for image processing, such as OpenCV, can take advantage of GPUs' parallel processing capabilities for Image image processing.
Incorporating Intel's oneAPI DAL into image processing applications is only worth it when massive datasets of Image Data need processing or very computationally intensive operations such as image compression, dimensionality reduction, or feature extraction is required. DAL's optimized algorithms provide a performance advantage over other libraries when performing heavy linear algebra and statistical functions. DAL also offers excellent flexibility for creating Custom algorithms.
