Design Philosophy and Usage Scenarios
Common Mistake"The GPU is better than the CPU."
- Not true as they're good at different things.
- The CPU is still essential for general tasks and system control.
Think of it like this:
- The CPU is the brain, excellent at decision-making and multitasking.
- The GPU is the muscle team, built to do lots of the same job, fast and in parallel.
An Orchestra
- CPU = The Conductor: makes high-level decisions, coordinates everything.
- GPU = The Orchestra: hundreds of players (cores), each playing the same sheet music (data) in perfect parallel.
CPU Design Philosophy
Flexibility and Generalization
- CPUs are designed to handle a wide variety of tasks, from running operating systems to executing complex algorithms.
- They excel at sequential processing, making them ideal for tasks that require complex logic and decision-making.
CPUs are optimized for low latency, meaning they prioritize completing individual tasks quickly, even if it means handling fewer tasks simultaneously.
Key Features
- Fewer, More Powerful Cores: Each core is capable of handling complex instructions with high clock speeds.
- Branch Prediction: CPUs use advanced techniques to anticipate the next instruction, minimizing delays.
- Instruction Versatility: They support a broad set of instructions, making them suitable for general-purpose computing.
Think of a CPU as a master chef in a kitchen, capable of handling a wide range of tasks with precision and expertise.
GPU Design Philosophy
High Throughput and Parallelism
- GPUs are designed for tasks that can be broken down into smaller, independent pieces.
- They excel at parallel processing, making them ideal for graphics rendering, scientific simulations, and machine learning.
GPUs are optimized for high throughput, meaning they can process large amounts of data simultaneously.
Key Features
- Thousands of Cores: Each core is less powerful than a CPU core but designed for simple, repetitive tasks.
- SIMD Architecture: GPUs use Single Instruction, Multiple Data (SIMD) operations to apply the same instruction to many data elements at once.
- High Memory Bandwidth: Designed to move data efficiently between cores and memory.
Imagine a GPU as a team of line cooks in a restaurant, each handling a specific task like chopping vegetables or grilling meat, all working simultaneously to prepare a meal.
Usage Scenarios
- CPUs
- Running operating systems and managing system resources
- Executing general-purpose software (e.g., web browsers, office applications)
- Handling user input and multitasking
- GPUs
- Graphics rendering for gaming and video editing
- Accelerating scientific simulations and machine learning
- Processing large datasets in parallel
- When designing a system, consider the nature of the tasks.
- Use CPUs for sequential, logic-intensive operations and GPUs for parallel, data-intensive tasks.
Core Architecture, Processing Power, and Memory Access
Core Architecture
- CPUs
- Fewer, More Powerful Cores: Typically 4–16 cores, each capable of handling complex instructions.
- High Clock Speeds: Enable fast execution of sequential tasks.
- Advanced Features: Include branch prediction and out-of-order execution.
- Limited Parallelism: Relying more on multi-threading
- GPUs
- Massive Parallelism: Hundreds to thousands of cores designed for simple, repetitive tasks.
- SIMD Capabilities: Allow the same operation to be performed on multiple data points simultaneously.
- Specialized Cores: Some GPUs include tensor cores for machine learning.
While individual GPU cores are less powerful than CPU cores, the sheer number of GPU cores enables tremendous parallel processing power.
Processing Power
- CPUs
- Optimized for Sequential Tasks: High instructions per cycle (IPC) and multithreading capabilities.
