Before delving into why GPUs are crucial for accelerated computing, let’s grasp how a CPU (Central Processing Unit) operates.

A CPU serves as a computer’s brain, executing instructions through arithmetic and logical operations while coordinating tasks. Its data processing involves ingesting, storing, and eventually processing data to yield results—data in, information out. However, this process employs serial processing, handling data sequentially. This approach becomes inefficient when dealing with extensive datasets, consuming considerable time.

Modern CPUs feature multiple high-performance cores for multicore processing, enabling parallel processing to enhance efficiency as these cores independently handle data. Yet, due to the CPU’s original design limitations in performing parallel activities, scaling becomes challenging, often bottlenecked by memory bandwidth. This limitation makes larger datasets problematic for CPU processing.

This is where GPUs come into play. A GPU is specialized hardware tailored for specific types of calculations. Unlike CPUs, GPUs excel at executing a few simple operations rapidly. Comprising thousands of lightweight cores, GPUs conduct parallel processing, allowing them to swiftly process large datasets compared to CPUs.