Parallel Computing
High-performance computing
High-performance computing (HPC) is the practice of aggregating large numbers of processors, accelerators, memory, and storage into tightly interconnected systems to solve computation- and data-intensive problems. It encompasses supercomputers and clusters, specialized networks and file systems, and software stacks designed for parallelism, and is used across fields such as weather forecasting, materials science, energy, and artificial intelligence.
Parallel AI
Parallel AI refers to the application of parallel and distributed computing techniques to artificial intelligence workloads, especially the training and serving of large machine-learning models. The approach encompasses data, model, pipeline, and expert parallelism, along with optimizer and memory sharding, to scale computation across multi-GPU, multi-node, and heterogeneous systems.
Parallel computing
Parallel computing is the design and use of computer systems that perform multiple computations simultaneously by dividing a problem into parts executed at the same time. It underpins high‑performance computing, large‑scale data processing, and modern AI/ML workloads, and spans architectures from multicore CPUs and GPUs to distributed clusters and supercomputers.