Technology trends in computing hardware and their impacts on high-performance scientific computing Part II: Memory systems, interconnects, and system integration

In Part I of this paper (Hanindhito et al., 2026), we argued understanding technology trends in computing hardware is necessary for designing next-generation algorithms for scientific computing. Using a language that is accessible to a general computational or data scientist, or applied mathematician, we aim to help our targeted readers better understand how technology trends in computing hardware are going to impact their computations, and what characteristics algorithms should exhibit to best harness modern hardware. In Part I, we covered background material, general-purpose processors, and hardware accelerators. In Part II, we review memory systems, inter-device communication, heterogeneous computing and system integration, and why energy efficiency has become a central issue in hardware design, and why it cannot be ignored in cluster-sized computers. We conjecture how the above changes may impact scientific computing, and offer options for leveraging modern hardware for both old and modern scientific software.