Is it true that the GPU is effective for processing easy-to-parallel programs requiring intensive computing?

The statement is accurate because Graphics Processing Units (GPUs) are designed specifically for parallel processing, making them exceedingly effective for tasks that can be broken down into smaller, independent operations. This capability enables GPUs to handle a large volume of calculations simultaneously, which is particularly beneficial for applications in artificial intelligence, machine learning, and graphic rendering.

Programs that require intensive computing tasks, such as matrix multiplications in neural networks or image processing algorithms, often involve operations that can be computed in parallel. This allows a GPU to leverage its architecture, which consists of hundreds or thousands of small processing cores, to execute many threads at once. Thus, for programs that can utilize this parallelism, GPUs deliver substantial speedups compared to traditional central processing units (CPUs) that are optimized for sequential processing.