Natural antioxidants are more attractive than synthetic chemical oxidants because of their non-toxic and non-harmful properties. Microalgal bioactive components such as carotenoids, polysaccharides, and phenolic compounds are gaining popularity as very effective and long-lasting natural antioxidants. Few articles currently exist that analyze microalgae from a bibliometric and visualization point of view. This study used a bibliometric method based on the Web of Science Core Collection database to analyze antioxidant research on bioactive compounds in microalgae from 1996 to 2022. According to cluster analysis, the most studied areas are the effectiveness, the antioxidant mechanism, and use of bioactive substances in microalgae, such as carotene, astaxanthin, and tocopherols, in the fields of food, cosmetics, and medicine. Using keyword co-occurrence and keyword mutation analysis, future trends are predicted to improve extraction rates and stability by altering the environment of microalgae cultures or mixing extracts with chemicals such as nanoparticles for commercial and industrial applications. These findings can help researchers identify trends and resources to build impactful investigations and expand scientific frontiers.

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Structured grid-based sparse matrix-vector multiplication and Gauss–Seidel iterations are very important kernel functions in scientific and engineering computations, both of which are memory intensive and bandwidth-limited. GPDSP is a general purpose digital signal processor, which is a very significant embedded processor that has been introduced into high-performance computing. In this paper, we designed various optimization methods, which included a blocking method to improve data locality and increase memory access efficiency, a multicolor reordering method to develop Gauss–Seidel fine-grained parallelism, a data partitioning method designed for GPDSP memory structures, and a double buffering method to overlap computation and access memory on structured grid-based SpMV and Gauss–Seidel iterations for GPDSP. At last, we combined the above optimization methods to design a multicore vectorization algorithm. We tested the matrices generated with structured grids of different sizes on the GPDSP platform and obtained speedups of up to 41× and 47× compared to the unoptimized SpMV and Gauss–Seidel iterations, with maximum bandwidth efficiencies of 72% and 81%, respectively. The experiment results show that our algorithms could fully utilize the external memory bandwidth. We also implemented the commonly used mixed precision algorithm on the GPDSP and obtained speedups of 1.60× and 1.45× for the SpMV and Gauss–Seidel iterations, respectively.