Difference between GPU and CPU

Let’s look at some of the main differences between GPU and CPU in this post. CPU is often referred to as the “brain” of the computer, the CPU handles general-purpose tasks such as running the operating system, executing application code, and managing I/O operations. It is designed for high single-threaded performance (tasks done one after the other).

On the other hand, GPU was originally designed for graphics rendering, a GPU is now used for specialized computations like deep learning, scientific simulations, and parallel processing. It excels at parallel processing where many tasks can be handled simultaneously, such as matrix multiplications and vector operations.

 

Architecture

• CPU: CPU typically has 4 to 16 cores (though high-end servers may have more), each optimized for complex tasks performed sequentially. Each core is powerful and versatile, handling a wide range of operations.
• GPU: GPU has hundreds to thousands of smaller cores designed for parallel processing. It excels in tasks like rendering images and training machine learning models where many simple calculations are performed at once.

Processing Style

• CPU: Performs sequential processing, great for tasks requiring quick decision-making and complex branching, where one task needs to be completed before the next one starts.
• GPU: Performs parallel processing, ideal for tasks that can be broken into many smaller, independent operations (e.g., neural network training, graphics rendering).

Performance

• CPU: Offers high single-core performance, best for tasks needing low latency and high versatility, such as running operating systems and office applications.
• GPU: Performs exceptionally well for parallel processing tasks, significantly accelerating deep learning model training, rendering, and large-scale computations.

Task Suitability

• CPU: General-purpose computing, such as browsing the web, word processing, and running most desktop applications.
• GPU: Specialized computing, including graphics rendering, machine learning, video processing, and cryptocurrency mining.

Example Applications

• CPU: Running operating systems (Windows, Linux, macOS), executing programs like word processors and web browsers, handling tasks requiring complex logic.
• GPU: Rendering graphics in video games, running deep learning algorithms (TensorFlow, PyTorch), video editing, and cryptocurrency mining.

Energy Efficiency

• CPU: More energy-efficient for single-threaded tasks but consumes more power overall for parallel workloads.
• GPU: Power-hungry, but highly efficient for tasks like rendering and machine learning, offering performance-per-watt advantages.

Memory

• CPU: Uses shared system memory (RAM) for tasks.
• GPU: Uses dedicated high-bandwidth memory (VRAM), optimized for large-scale tasks like rendering and deep learning computations.

Differences

Feature	CPU	GPU
Core Count	Few cores (typically 4-16)	Many cores (hundreds to thousands)
Core Type	Powerful, versatile cores	Smaller, simpler cores for parallel tasks
Processing Style	Sequential processing (single-threaded)	Parallel processing (multi-threaded)
Task Suitability	General-purpose tasks (logic, I/O)	Parallel tasks (graphics, AI, ML)
Performance	High single-core performance	High parallel performance
Examples	Web browsing, office apps, OS	Gaming, video editing, AI/ML, simulations
Energy Efficiency	More efficient for single-threaded tasks	More efficient for parallel tasks
Memory	Shared system memory (RAM)	Dedicated high-bandwidth memory (VRAM)