👉 Mug computing, also known as MUG (Multi-GPU Unified Computing), is a computational paradigm that leverages multiple GPUs in a single system to achieve parallel processing and significantly enhance computational efficiency. This approach involves distributing tasks across numerous GPUs, which can work concurrently to process large datasets or complex algorithms more rapidly than a single GPU could handle. MUG aims to optimize resource utilization, reduce latency, and improve performance in data-intensive applications such as machine learning, scientific simulations, and graphics rendering. By integrating these GPUs into a cohesive computing unit, Mug computing simplifies programming and management while maximizing throughput and scalability, making it particularly useful for tasks that require high computational power and parallelism.
