Heterogeneous-Computing

Hardware Acceleration: GPUs, TPUs, and Beyond Hardware acceleration uses dedicated devices to run heavy tasks more efficiently than a plain CPU. GPUs excel at many simple operations in parallel, while TPUs focus on fast tensor math for neural networks. Other accelerators, such as FPGAs and ASICs, offer specialized strengths. Together, they speed up graphics, data processing, and AI workloads across clouds, desktops, and edge devices. Choosing the right tool means weighing what you need. GPUs are versatile and widely supported, with mature libraries for machine learning and high-performance computing. TPUs deliver strong tensor performance for large models in ideal cloud setups. Other accelerators can cut power use or speed narrow parts of a pipeline, but may require more development work. ...

Hardware Accelerators: GPUs, TPUs and Beyond Hardware accelerators shape how we train and deploy modern AI. GPUs, TPUs and beyond offer different strengths for different tasks. This guide explains the main options and practical tips to choose the right tool for your workload. GPUs are built for parallel work. They have many cores, high memory bandwidth, and broad software support. They shine in training large models and in research where flexibility matters. With common frameworks like PyTorch and TensorFlow, you can use mixed precision to speed up training while keeping accuracy. In practice, a single GPU or a few can handle experiments quickly, and cloud options make it easy to scale up. ...