Interconnects

Hardware Architecture for High Performance Computing High performance computing (HPC) loads demand fast processors, generous memory, and high bandwidth networks. The hardware architecture sets the ceiling for how quickly simulations run and how well software scales across thousands of cores. Key building blocks include: CPUs with many cores, large caches, and good single‑thread performance GPUs or other accelerators to handle massive parallel work fast memory options, from DDR4/5 to high‑bandwidth memory (HBM) on accelerators high‑speed interconnects and a scalable network topology robust storage and parallel file systems to feed data Memory hierarchy matters. Cache levels reduce latency, while NUMA domains require careful memory placement. On GPUs, HBM provides enormous bandwidth, but data movement between host and device still matters for performance. ...

Building High-Performance Hardware for AI and Data Building high-performance AI hardware starts with a clear view of the workload. Are you training large models, running many inferences, or both? The answer guides choices for compute, memory, and data movement. Training favors many GPUs with fast interconnects; inference benefits from compact, energy-efficient accelerators and memory reuse. Start by mapping your pipeline: data loading, preprocessing, model execution, and result storage. Core components matter. Choose accelerators (GPUs, TPUs, or other AI chips) based on the workload, then pair them with fast CPUs for orchestration. Memory bandwidth is king: look for high-bandwidth memory (HBM) or wide memory channels, along with a sensible cache strategy. Interconnects like PCIe 5/6, NVLink, and CXL affect latency and scale. Storage should be fast and reliable (NVMe SSDs, tiered storage). Networking is essential for multi-node training and large data transfers (think 100G+ links). ...