Hybrid CPU GPU

Hybrid CPU-GPU computing is increasingly crucial for accelerating computationally intensive tasks, particularly in machine learning and scientific simulations. Current research focuses on optimizing data transfer and workload distribution between CPUs and GPUs, employing techniques like intelligent caching and task assignment algorithms to minimize bottlenecks and maximize performance. This is evident in applications ranging from large-scale recommendation systems and multi-agent planning to quantum simulations, where hybrid architectures significantly improve training speed and accuracy compared to CPU-only approaches. The resulting performance gains are transforming various fields by enabling the efficient processing of previously intractable datasets and models.