Heterogeneous Computing
Heterogeneous computing is a computing paradigm that utilizes multiple types of processing units, such as CPUs, GPUs, FPGAs, and specialized accelerators, within a single system to optimize performance, energy efficiency, or cost for specific workloads. It involves distributing tasks across these diverse hardware components based on their strengths, such as using GPUs for parallel processing or FPGAs for custom logic. This approach is common in high-performance computing, artificial intelligence, and data-intensive applications where traditional CPU-only systems are insufficient.
Developers should learn heterogeneous computing when working on computationally intensive tasks like machine learning training, scientific simulations, or real-time data processing, as it can significantly accelerate performance and reduce energy consumption. It is essential in fields like AI/ML (using GPUs for neural networks), gaming (combining CPUs and GPUs), and edge computing (leveraging specialized chips), where leveraging the right hardware for specific tasks leads to better scalability and efficiency.