The field of high-performance computing is witnessing significant advancements in scalable computing and tensor processing. Researchers are exploring innovative approaches to improve the strong scaling of molecular dynamics simulations, enabling faster time-to-solution on heterogeneous supercomputers. Furthermore, there is a growing focus on developing distributed frameworks for multimodal image registration, allowing for the analysis of large-scale datasets. The development of performance-portable compiler frameworks is also gaining traction, enabling the integration of codes from different frameworks and facilitating extensibility to new architectures. Additionally, libraries for interactive GPU-accelerated large tensor processing and visualization are being developed, enabling researchers to efficiently process and visualize large datasets. Noteworthy papers in this area include: LAPIS, a performance-portable compiler framework that addresses the challenges of portability, performance, and productivity. Palace, a library for interactive GPU-accelerated large tensor processing and visualization that enables the development of out-of-core pipelines on workstation hardware.