Differences Between CPU and GPU

Explore the key differences between a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit):

1. Purpose and Function:
• CPU: The CPU acts as the “brain” of a computer system. It comprises the Arithmetic Logic Unit (ALU) for calculations and the Control Unit (CU) for instruction sequencing and branching. CPUs interact with various components like memory, input, and output devices.
• GPU: GPUs are primarily used to render images in computer games. They focus on high throughput and are faster than CPUs. GPUs often share RAM with electronic equipment, making them suitable for compute-intensive tasks.
2. Memory Usage:
• CPU: CPUs consume more memory than GPUs.
• GPU: GPUs require less memory compared to CPUs.
3. Speed:
• CPU: CPUs have lower speed compared to GPUs.
• GPU: GPUs operate at a faster speed.
4. Cores:
• CPU: CPUs have a few powerful cores.
• GPU: GPUs contain many weaker cores.
5. Instruction Processing:
• CPU: Suitable for serial instruction processing.
• GPU: Not suitable for serial instruction processing; excels in parallel instruction processing.
6. Parallelism:
• CPU: Not ideal for parallel instruction processing.
• GPU: Designed for parallel instruction processing.
7. Emphasis:
• CPU: Emphasizes low latency.
• GPU: Emphasizes high throughput.

In summary, CPUs handle a wide range of tasks quickly, while GPUs excel at running many smaller tasks concurrently. CPUs are essential for interactivity, while GPUs break down complex problems into parallel tasks for efficient execution.

Comparative roles of CPU and GPU

CPUs (Central Processing Units) and GPUs (Graphics Processing Units) play distinct roles in deep learning. Let’s explore how they collaborate:

1. Training Phase:
• CPU: During training, the CPU manages high-level tasks such as loading data, preprocessing, and orchestrating the overall training process.
• GPU: The GPU is the workhorse for training deep neural networks. It performs parallel computations on large matrices, which are fundamental to neural network training. GPUs excel at matrix multiplications, convolutions, and other mathematical operations.
2. Parallelism:
• CPU: CPUs are designed for serial execution of instructions. They handle one task at a time.
• GPU: GPUs shine in parallel execution. They have thousands of small cores that can simultaneously process multiple threads. Deep learning models involve millions of calculations, making GPUs ideal for parallelism.
3. Matrix Operations:
• CPU: CPUs can perform matrix operations but are slower due to their limited number of cores.
• GPU: GPUs are optimized for matrix operations. Their massive parallelism accelerates matrix multiplications, essential for neural network training.
4. Memory Transfer:
• CPU: Handles data transfer between RAM, storage, and GPU memory.
• GPU: Efficiently moves data between CPU and GPU memory, minimizing latency during training.
5. Model Inference (Prediction):
• CPU: For inference (using a trained model to make predictions), CPUs are sufficient. They handle individual predictions efficiently.
• GPU: GPUs can accelerate inference too, especially when speed is critical (e.g., real-time applications). However, the cost-effectiveness depends on the specific use case.
6. Deployment:
• CPU: Deploying models on CPUs is cost-effective for inference tasks with moderate throughput requirements.
• GPU: When inference speed is crucial, GPUs provide substantial gains. They offer better throughput and stability, even though they are more expensive

In summary, CPUs and GPUs work together in deep learning by leveraging their strengths: CPUs manage orchestration, while GPUs handle intensive computations during training and inference. The choice depends on the specific task, cost considerations, and performance requirements.